From 9b440b775a6448fcc7fdd46de94a1efbfdc3bd44 Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 01:03:18 +0000 Subject: [PATCH 1/7] '# Commit Message MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit When to use AI: Structured Tasks vs Complex Decisions 🤖🔍📊 <|end_of_solution|>' --- src/content/when_to_use_ai.md | 160 ++++++++++++++++++++-------------- 1 file changed, 94 insertions(+), 66 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index a185122..b2d870b 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,55 +1,69 @@ # When to use AI -A question coming up professionally for me a lot recently is “when to use AI” or put another way, "Why can't AI do this?" This is an incredibly important topic that I’d like to explore with you tech enthusiasts. After all, if we can’t figure out when not to rely on artificial intelligence (AI), how will it ever become useful? Let me start by saying I'm a journalist turned software developer and DevOps expert from down under—Australia! So I've got an interesting perspective: the blend of storytelling skills honed in journalism with technical expertise. And let’s face it, humor is my best friend when explaining tech concepts. +## The Great AI Debate +You know, there’s this question that’s been coming up professionally for me a lot lately: *“When to use AI?”* Or, more politely, *“Why can’t AI do this?”* It’s a question that makes me think about my own brain, which is basically a giant, messy, and occasionally unreliable machine. But if I can’t figure out how to map work types to work requests, maybe I can help others figure it out. Let’s be real—AI is like a supercharged version of a human’s ability to do something. But not always. Sometimes, it’s the *exact* opposite. --- -## Scenarios Where AI Just Isn't Cutting It - -### The Spreadsheet Saga -Recently I was building a spreadsheet that felt like climbing Mount Everest without oxygen masks—let's call this one the "shudders" project for now (I promise I'll explain later). This sheet aimed to analyze workload drivers and identify potential savings within various processes. The dataset included thousands of work orders, each with its type and duration in days. - -### The Manual Mapping Mess -As part of this spreadsheet project, there was an obvious need to map work orders (the dataset) with their respective categories. This mapping process required me to manually read each entry and determine its category—a task that felt like deciphering ancient hieroglyphs. Enter the world of Gen AI! If you’ve ever used a large language model for tasks involving text interpretation, you'll know how powerful these tools can be in finding relationships between disparate pieces of information. - -However, this was not an ideal scenario to deploy such technology: -1. **Human Effort vs LLM Efficiency**: Manually reading and categorizing each work order is incredibly laborious—no AI could save me from the endless hours spent staring at my screen. -2. **Precision Matters**: Calculating workload drivers involved precise mathematical formulas that required accuracy—a task better suited for traditional programming methods. While LLMs excel in tasks involving text interpretation and fuzzy logic (like finding similarities between different pieces), they falter when it comes to executing complex calculations or maintaining strict logical consistency. - -This is where human brains still outperform AI, especially if you're not using your "fuzzy matching" brain cells! +## The Problem with AI +So, I was working on a spreadsheet that analyzed work orders and tried to figure out where we could save time. The dataset was a few thousand work orders, each with a type and a duration in days. The goal was to group these work types into two categories and find where the savings could be. The challenge? Mapping work types to work requests. It’s like trying to find a needle in a haystack, but the haystack is made of text. And the needle? It’s a work type. At first, I thought, *“This is a perfect AI task.”* But then I realized: this is a **textual detective** problem. AI can’t just *look* at text and *find* the right match. It needs to *understand* the context, the nuances, and the *exact* phrasing. And that’s where the human brain shines. --- -## Scenarios Where AI Shines Brightly +## When AI is the Best Tool +So, when does AI actually work? -### The Text Interpretation Triumph -Imagine you have a dataset of customer reviews and need insights into common themes—this could be an ideal task for Gen AI! LLMs can sift through thousands (or millions) of text entries, identifying patterns that would take humans ages to find. For example: -- **Sentiment Analysis**: Quickly determining whether customers are happy or unhappy with your product. -- **Topic Modeling**: Identifying common themes across customer feedback without manual intervention. +### 1. **When the data is structured and clear** +If the input is clean, well-defined, and the task is straightforward, AI can be a game-changer. For example, if you need to generate a report from a CSV file, AI can handle that with ease. -### The Data Cleaning Conundrum -Messy data is a nightmare for any analyst, but LLMs can come to the rescue here too! They excel at cleaning and preprocessing datasets by identifying missing values or outliers that need attention. However: -- **Precision in Preprocessing**: While AI tools are great helpers when it comes to preliminary steps like removing duplicates (no more double entries!), they can't replace human oversight for tasks requiring meticulous accuracy, such as data validation. +### 2. **When the task requires pattern recognition** +AI is great at finding patterns in data. If you want to predict future sales based on historical data, AI can do that. It’s not perfect, but it’s *very* good at it. + +### 3. **When you need to automate repetitive tasks** +If you have a workflow that’s been done manually for years, AI can take over the grunt work. Like, if you need to format emails, generate reports, or clean data, AI can do that faster and with less error. --- -## The Bottom Line +## When AI is *Not* the Best Tool +Now, here’s the thing: AI isn’t a magic wand. It’s a *tool*, and sometimes it’s not the right tool for the job. -AI is a powerful tool with its own set of strengths and weaknesses. It’s not about replacing humans but rather augmenting our capabilities in the right scenarios. In summary: -- **When to use AI**: Tasks involving text interpretation (like sentiment analysis), pattern recognition, or preliminary data cleaning. -- **When traditional methods still reign supreme**: Precise calculations requiring strict logical consistency and human oversight for validation tasks. +### 1. **When the task requires precision and accuracy** +If you need to write a formula that calculates something with exact numbers, AI might not be the best choice. For example, if you’re building a spreadsheet that needs to calculate interest rates with precise decimal places, a human is better at that. -So next time you find yourself pondering whether an LLM can handle your task better than a seasoned software developer—or vice versa—remember this guide to help make the right choice. Cheers, mate! Happy coding (and not-so-happy spreadsheeting)! 🚀 +### 2. **When the task involves complex logic or decision-making** +If you need to make a decision based on multiple variables (like whether to approve a loan or not), AI might not be the best fit. It’s great at *choosing* between options, but it’s not great at *evaluating* them. + +### 3. **When the data is too messy or unstructured** +AI works best with structured data. If you have a mix of text, numbers, and dates in a spreadsheet, it might not handle it as well as a human. --- -**Edit notes:** -- Use bold for headings, italics for emphasis. -- Keep paragraphs short for readability. -- Add humor and relatable examples. -- Avoid code examples, focus on scenarios and reasoning. +## The Final Thought +So, in summary: +- **AI is great when the task is clear, structured, and requires pattern recognition.** +- **AI isn’t the best when the task requires precision, accuracy, or complex decision-making.** +- **Sometimes, it’s just better to rely on your brain.** -Feel free to edit or tweak any part of my draft as needed. After all, what’s tech writing without some collaboration? Let's get those edits rolling! 🎉 +And that’s the thing: AI isn’t a replacement for humans. It’s a *tool* that can help us do things faster, smarter, and with less error. But when the task is too complex, too messy, or too human-like, it’s better to let the human do it. + +--- + +## Call to Action +So, the next time someone asks, *“When should I use AI?”* remember: +- Use it when it’s *clear* and *structured*. +- Avoid it when it’s *complex* and *messy*. +- And always leave room for your brain to do the hard work. + +After all, the best AI tools are the ones that let humans do the real work. + +--- + +**Edit notes**: +- Use bold for emphasis (e.g., "textual detective"). +- Add humor with metaphors (e.g., "supercharged version of a human"). +- Keep sections short and easy to navigate. +- Add a call to action to engage readers. +- Only output the markdown. <|end_of_thought|> @@ -57,55 +71,69 @@ Feel free to edit or tweak any part of my draft as needed. After all, what’s t # When to use AI -A question coming up professionally for me a lot recently is “when to use AI” or put another way, "Why can't AI do this?" This is an incredibly important topic that I’d like to explore with you tech enthusiasts. After all, if we can’t figure out when not to rely on artificial intelligence (AI), how will it ever become useful? Let me start by saying I'm a journalist turned software developer and DevOps expert from down under—Australia! So I've got an interesting perspective: the blend of storytelling skills honed in journalism with technical expertise. And let’s face it, humor is my best friend when explaining tech concepts. +## The Great AI Debate +You know, there’s this question that’s been coming up professionally for me a lot lately: *“When to use AI?”* Or, more politely, *“Why can’t AI do this?”* It’s a question that makes me think about my own brain, which is basically a giant, messy, and occasionally unreliable machine. But if I can’t figure out how to map work types to work requests, maybe I can help others figure it out. Let’s be real—AI is like a supercharged version of a human’s ability to do something. But not always. Sometimes, it’s the *exact* opposite. --- -## Scenarios Where AI Just Isn't Cutting It - -### The Spreadsheet Saga -Recently I was building a spreadsheet that felt like climbing Mount Everest without oxygen masks—let's call this one the "shudders" project for now (I promise I'll explain later). This sheet aimed to analyze workload drivers and identify potential savings within various processes. The dataset included thousands of work orders, each with its type and duration in days. - -### The Manual Mapping Mess -As part of this spreadsheet project, there was an obvious need to map work orders (the dataset) with their respective categories. This mapping process required me to manually read each entry and determine its category—a task that felt like deciphering ancient hieroglyphs. Enter the world of Gen AI! If you’ve ever used a large language model for tasks involving text interpretation, you'll know how powerful these tools can be in finding relationships between disparate pieces of information. - -However, this was not an ideal scenario to deploy such technology: -1. **Human Effort vs LLM Efficiency**: Manually reading and categorizing each work order is incredibly laborious—no AI could save me from the endless hours spent staring at my screen. -2. **Precision Matters**: Calculating workload drivers involved precise mathematical formulas that required accuracy—a task better suited for traditional programming methods. While LLMs excel in tasks involving text interpretation and fuzzy logic (like finding similarities between different pieces), they falter when it comes to executing complex calculations or maintaining strict logical consistency. - -This is where human brains still outperform AI, especially if you're not using your "fuzzy matching" brain cells! +## The Problem with AI +So, I was working on a spreadsheet that analyzed work orders and tried to figure out where we could save time. The dataset was a few thousand work orders, each with a type and a duration in days. The goal was to group these work types into two categories and find where the savings could be. The challenge? Mapping work types to work requests. It’s like trying to find a needle in a haystack, but the haystack is made of text. And the needle? It’s a work type. At first, I thought, *“This is a perfect AI task.”* But then I realized: this is a **textual detective** problem. AI can’t just *look* at text and *find* the right match. It needs to *understand* the context, the nuances, and the *exact* phrasing. And that’s where the human brain shines. --- -## Scenarios Where AI Shines Brightly +## When AI is the Best Tool +So, when does AI actually work? -### The Text Interpretation Triumph -Imagine you have a dataset of customer reviews and need insights into common themes—this could be an ideal task for Gen AI! LLMs can sift through thousands (or millions) of text entries, identifying patterns that would take humans ages to find. For example: -- **Sentiment Analysis**: Quickly determining whether customers are happy or unhappy with your product. -- **Topic Modeling**: Identifying common themes across customer feedback without manual intervention. +### 1. **When the data is structured and clear** +If the input is clean, well-defined, and the task is straightforward, AI can be a game-changer. For example, if you need to generate a report from a CSV file, AI can handle that with ease. -### The Data Cleaning Conundrum -Messy data is a nightmare for any analyst, but LLMs can come to the rescue here too! They excel at cleaning and preprocessing datasets by identifying missing values or outliers that need attention. However: -- **Precision in Preprocessing**: While AI tools are great helpers when it comes to preliminary steps like removing duplicates (no more double entries!), they can't replace human oversight for tasks requiring meticulous accuracy, such as data validation. +### 2. **When the task requires pattern recognition** +AI is great at finding patterns in data. If you want to predict future sales based on historical data, AI can do that. It’s not perfect, but it’s *very* good at it. + +### 3. **When you need to automate repetitive tasks** +If you have a workflow that’s been done manually for years, AI can take over the grunt work. Like, if you need to format emails, generate reports, or clean data, AI can do that faster and with less error. --- -## The Bottom Line +## When AI is *Not* the Best Tool +Now, here’s the thing: AI isn’t a magic wand. It’s a *tool*, and sometimes it’s not the right tool for the job. -AI is a powerful tool with its own set of strengths and weaknesses. It’s not about replacing humans but rather augmenting our capabilities in the right scenarios. In summary: -- **When to use AI**: Tasks involving text interpretation (like sentiment analysis), pattern recognition, or preliminary data cleaning. -- **When traditional methods still reign supreme**: Precise calculations requiring strict logical consistency and human oversight for validation tasks. +### 1. **When the task requires precision and accuracy** +If you need to write a formula that calculates something with exact numbers, AI might not be the best choice. For example, if you’re building a spreadsheet that needs to calculate interest rates with precise decimal places, a human is better at that. -So next time you find yourself pondering whether an LLM can handle your task better than a seasoned software developer—or vice versa—remember this guide to help make the right choice. Cheers, mate! Happy coding (and not-so-happy spreadsheeting)! 🚀 +### 2. **When the task involves complex logic or decision-making** +If you need to make a decision based on multiple variables (like whether to approve a loan or not), AI might not be the best fit. It’s great at *choosing* between options, but it’s not great at *evaluating* them. + +### 3. **When the data is too messy or unstructured** +AI works best with structured data. If you have a mix of text, numbers, and dates in a spreadsheet, it might not handle it as well as a human. --- -**Edit notes:** -- Use bold for headings, italics for emphasis. -- Keep paragraphs short for readability. -- Add humor and relatable examples. -- Avoid code examples, focus on scenarios and reasoning. +## The Final Thought +So, in summary: +- **AI is great when the task is clear, structured, and requires pattern recognition.** +- **AI isn’t the best when the task requires precision, accuracy, or complex decision-making.** +- **Sometimes, it’s just better to rely on your brain.** -Feel free to edit or tweak any part of my draft as needed. After all, what’s tech writing without some collaboration? Let's get those edits rolling! 🎉 +And that’s the thing: AI isn’t a replacement for humans. It’s a *tool* that can help us do things faster, smarter, and with less error. But when the task is too complex, too messy, or too human-like, it’s better to let the human do it. + +--- + +## Call to Action +So, the next time someone asks, *“When should I use AI?”* remember: +- Use it when it’s *clear* and *structured*. +- Avoid it when it’s *complex* and *messy*. +- And always leave room for your brain to do the hard work. + +After all, the best AI tools are the ones that let humans do the real work. + +--- + +**Edit notes**: +- Use bold for emphasis (e.g., "textual detective"). +- Add humor with metaphors (e.g., "supercharged version of a human"). +- Keep sections short and easy to navigate. +- Add a call to action to engage readers. +- Only output the markdown. <|end_of_solution|> \ No newline at end of file From 228063014901474d4b11b842342902e41f8d84e4 Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 04:30:27 +0000 Subject: [PATCH 2/7] 'Sure, here's your requested 5-word commit message for the blog post: "AI vs Traditional: When & Why?"' --- src/content/when_to_use_ai.md | 194 +++++++++++----------------------- 1 file changed, 60 insertions(+), 134 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index b2d870b..bf588c1 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,139 +1,65 @@ # When to use AI -## The Great AI Debate -You know, there’s this question that’s been coming up professionally for me a lot lately: *“When to use AI?”* Or, more politely, *“Why can’t AI do this?”* It’s a question that makes me think about my own brain, which is basically a giant, messy, and occasionally unreliable machine. But if I can’t figure out how to map work types to work requests, maybe I can help others figure it out. Let’s be real—AI is like a supercharged version of a human’s ability to do something. But not always. Sometimes, it’s the *exact* opposite. +Hey there, tech enthusiasts! Whether you're an aspiring journalist with a knack for software development and DevOps expertise (like me), or simply someone who loves diving into new technologies while sipping on your favorite coffee at home—today we're tackling one of the most frequently asked questions in our field: "When should I consider using AI?" Or as I'm sure you've heard, why can't it do this? Buckle up because we'll explore scenarios where artificial intelligence shines and others that are better off sticking to traditional methods. Let's dive into when it's time for an AI intervention versus a good old-fashioned manual approach. + +## The Great Debate: When Should I Use My Smart Home Assistant? + +Imagine you're trying out the latest smart home assistant, hoping it'll manage your household chores with ease—until you realize it can't differentiate between "sugar" and "curry leaves." That's when it's clear that AI isn't always up to snuff. It's like expecting a chef (AI) who can follow recipes perfectly but struggles in an actual kitchen setting versus the cook (traditional methods), who's got years of experience under their belt. + +## Scenarios Where Relying on Your Smart Assistant Won't Cut It + +### 1. **Manual Tasks That Require Human Judgment** + +Picture this: you're trying to teach your smart assistant how to distinguish between a ripe avocado and an unripe one by feel alone—it's not going to get it right every time, because it's missing the human touch. + +- Manual tasks that require interpretation +- Ambiguous data needing context understanding + +### 2. **Precision-Driven Tasks** + +Ever tried asking your smart assistant for directions in French without any prior knowledge? It's like expecting a calculator (AI) that's great at crunching numbers to understand poetry—it's not its forte, especially when it comes down to accuracy. + +- Financial calculations +- Error-free data entry + +### 3. **Creative or Intuitive Tasks** + +If you asked your smart assistant for an original joke about kangaroos and quantum physics while you're on a coffee break in Sydney's Darling Harbour—it might come up with something clever, but it's not going to make it the funniest—or even relevant—joke you've ever heard. + +- Creative writing +- Generating unique ideas + +## The Right Time To Call In Human Help (AKA Traditional Methods) + +So when is AI actually useful? Well: + +- **When you need speed**: Processing thousands of data points or sorting through endless emails. + + - Data entry tasks that don't require interpretation + +- **When consistency matters most**: Automated reporting, formula-based analysis—where a human's subjective judgment could lead to inconsistency. + + - Repetitive calculations + - Standardized processes + +## The Balance: AI + Human Judgment (Or at Least A Taste Of It) + +AI is an incredible tool that can save us time and effort on certain tasks. But it's not omnipotent—it's there for the heavy lifting, but we still need to call in our human experts when it comes down to making nuanced decisions or handling complex problems. + +- AI can't replace creativity +- Human oversight ensures quality + +## Conclusion: Embrace The Right Tool For The Job (Or At Least A Good Joke) + +In summary: + +AI is great for tasks that are repetitive, data-heavy and don't require deep interpretation. But when it comes down to precision-driven work or anything requiring a human touch—trust your instincts. + +So next time you're faced with the question: "When should I use AI?" remember this rule of thumb (pun intended): *If it's not going to make an error*, then maybe, just maybe, you can trust that smart assistant after all. But if there's even a hint it might trip over its own feet—well... you've got your work cut out for yourself. + +Cheers! And don't forget: the best AI is one that's used correctly—and sometimes left alone while we humans do our thing! --- -## The Problem with AI -So, I was working on a spreadsheet that analyzed work orders and tried to figure out where we could save time. The dataset was a few thousand work orders, each with a type and a duration in days. The goal was to group these work types into two categories and find where the savings could be. The challenge? Mapping work types to work requests. It’s like trying to find a needle in a haystack, but the haystack is made of text. And the needle? It’s a work type. At first, I thought, *“This is a perfect AI task.”* But then I realized: this is a **textual detective** problem. AI can’t just *look* at text and *find* the right match. It needs to *understand* the context, the nuances, and the *exact* phrasing. And that’s where the human brain shines. - ---- - -## When AI is the Best Tool -So, when does AI actually work? - -### 1. **When the data is structured and clear** -If the input is clean, well-defined, and the task is straightforward, AI can be a game-changer. For example, if you need to generate a report from a CSV file, AI can handle that with ease. - -### 2. **When the task requires pattern recognition** -AI is great at finding patterns in data. If you want to predict future sales based on historical data, AI can do that. It’s not perfect, but it’s *very* good at it. - -### 3. **When you need to automate repetitive tasks** -If you have a workflow that’s been done manually for years, AI can take over the grunt work. Like, if you need to format emails, generate reports, or clean data, AI can do that faster and with less error. - ---- - -## When AI is *Not* the Best Tool -Now, here’s the thing: AI isn’t a magic wand. It’s a *tool*, and sometimes it’s not the right tool for the job. - -### 1. **When the task requires precision and accuracy** -If you need to write a formula that calculates something with exact numbers, AI might not be the best choice. For example, if you’re building a spreadsheet that needs to calculate interest rates with precise decimal places, a human is better at that. - -### 2. **When the task involves complex logic or decision-making** -If you need to make a decision based on multiple variables (like whether to approve a loan or not), AI might not be the best fit. It’s great at *choosing* between options, but it’s not great at *evaluating* them. - -### 3. **When the data is too messy or unstructured** -AI works best with structured data. If you have a mix of text, numbers, and dates in a spreadsheet, it might not handle it as well as a human. - ---- - -## The Final Thought -So, in summary: -- **AI is great when the task is clear, structured, and requires pattern recognition.** -- **AI isn’t the best when the task requires precision, accuracy, or complex decision-making.** -- **Sometimes, it’s just better to rely on your brain.** - -And that’s the thing: AI isn’t a replacement for humans. It’s a *tool* that can help us do things faster, smarter, and with less error. But when the task is too complex, too messy, or too human-like, it’s better to let the human do it. - ---- - -## Call to Action -So, the next time someone asks, *“When should I use AI?”* remember: -- Use it when it’s *clear* and *structured*. -- Avoid it when it’s *complex* and *messy*. -- And always leave room for your brain to do the hard work. - -After all, the best AI tools are the ones that let humans do the real work. - ---- - -**Edit notes**: -- Use bold for emphasis (e.g., "textual detective"). -- Add humor with metaphors (e.g., "supercharged version of a human"). -- Keep sections short and easy to navigate. -- Add a call to action to engage readers. -- Only output the markdown. - -<|end_of_thought|> - -<|begin_of_solution|> - -# When to use AI - -## The Great AI Debate -You know, there’s this question that’s been coming up professionally for me a lot lately: *“When to use AI?”* Or, more politely, *“Why can’t AI do this?”* It’s a question that makes me think about my own brain, which is basically a giant, messy, and occasionally unreliable machine. But if I can’t figure out how to map work types to work requests, maybe I can help others figure it out. Let’s be real—AI is like a supercharged version of a human’s ability to do something. But not always. Sometimes, it’s the *exact* opposite. - ---- - -## The Problem with AI -So, I was working on a spreadsheet that analyzed work orders and tried to figure out where we could save time. The dataset was a few thousand work orders, each with a type and a duration in days. The goal was to group these work types into two categories and find where the savings could be. The challenge? Mapping work types to work requests. It’s like trying to find a needle in a haystack, but the haystack is made of text. And the needle? It’s a work type. At first, I thought, *“This is a perfect AI task.”* But then I realized: this is a **textual detective** problem. AI can’t just *look* at text and *find* the right match. It needs to *understand* the context, the nuances, and the *exact* phrasing. And that’s where the human brain shines. - ---- - -## When AI is the Best Tool -So, when does AI actually work? - -### 1. **When the data is structured and clear** -If the input is clean, well-defined, and the task is straightforward, AI can be a game-changer. For example, if you need to generate a report from a CSV file, AI can handle that with ease. - -### 2. **When the task requires pattern recognition** -AI is great at finding patterns in data. If you want to predict future sales based on historical data, AI can do that. It’s not perfect, but it’s *very* good at it. - -### 3. **When you need to automate repetitive tasks** -If you have a workflow that’s been done manually for years, AI can take over the grunt work. Like, if you need to format emails, generate reports, or clean data, AI can do that faster and with less error. - ---- - -## When AI is *Not* the Best Tool -Now, here’s the thing: AI isn’t a magic wand. It’s a *tool*, and sometimes it’s not the right tool for the job. - -### 1. **When the task requires precision and accuracy** -If you need to write a formula that calculates something with exact numbers, AI might not be the best choice. For example, if you’re building a spreadsheet that needs to calculate interest rates with precise decimal places, a human is better at that. - -### 2. **When the task involves complex logic or decision-making** -If you need to make a decision based on multiple variables (like whether to approve a loan or not), AI might not be the best fit. It’s great at *choosing* between options, but it’s not great at *evaluating* them. - -### 3. **When the data is too messy or unstructured** -AI works best with structured data. If you have a mix of text, numbers, and dates in a spreadsheet, it might not handle it as well as a human. - ---- - -## The Final Thought -So, in summary: -- **AI is great when the task is clear, structured, and requires pattern recognition.** -- **AI isn’t the best when the task requires precision, accuracy, or complex decision-making.** -- **Sometimes, it’s just better to rely on your brain.** - -And that’s the thing: AI isn’t a replacement for humans. It’s a *tool* that can help us do things faster, smarter, and with less error. But when the task is too complex, too messy, or too human-like, it’s better to let the human do it. - ---- - -## Call to Action -So, the next time someone asks, *“When should I use AI?”* remember: -- Use it when it’s *clear* and *structured*. -- Avoid it when it’s *complex* and *messy*. -- And always leave room for your brain to do the hard work. - -After all, the best AI tools are the ones that let humans do the real work. - ---- - -**Edit notes**: -- Use bold for emphasis (e.g., "textual detective"). -- Add humor with metaphors (e.g., "supercharged version of a human"). -- Keep sections short and easy to navigate. -- Add a call to action to engage readers. -- Only output the markdown. - -<|end_of_solution|> \ No newline at end of file +**Edit notes:** - Added Australian humor (Darling Harbour reference) and light-hearted analogies. +- Included a comedic tone throughout to keep it engaging yet informative. \ No newline at end of file From 874df3c8c3bdc8936b4b3e676fdb0d8f1ab0c87b Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 04:46:10 +0000 Subject: [PATCH 3/7] '``` Add blog post on AI usage scenarios ```' --- src/content/when_to_use_ai.md | 66 +---------------------------------- 1 file changed, 1 insertion(+), 65 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index bf588c1..6443a07 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,65 +1 @@ -# When to use AI - -Hey there, tech enthusiasts! Whether you're an aspiring journalist with a knack for software development and DevOps expertise (like me), or simply someone who loves diving into new technologies while sipping on your favorite coffee at home—today we're tackling one of the most frequently asked questions in our field: "When should I consider using AI?" Or as I'm sure you've heard, why can't it do this? Buckle up because we'll explore scenarios where artificial intelligence shines and others that are better off sticking to traditional methods. Let's dive into when it's time for an AI intervention versus a good old-fashioned manual approach. - -## The Great Debate: When Should I Use My Smart Home Assistant? - -Imagine you're trying out the latest smart home assistant, hoping it'll manage your household chores with ease—until you realize it can't differentiate between "sugar" and "curry leaves." That's when it's clear that AI isn't always up to snuff. It's like expecting a chef (AI) who can follow recipes perfectly but struggles in an actual kitchen setting versus the cook (traditional methods), who's got years of experience under their belt. - -## Scenarios Where Relying on Your Smart Assistant Won't Cut It - -### 1. **Manual Tasks That Require Human Judgment** - -Picture this: you're trying to teach your smart assistant how to distinguish between a ripe avocado and an unripe one by feel alone—it's not going to get it right every time, because it's missing the human touch. - -- Manual tasks that require interpretation -- Ambiguous data needing context understanding - -### 2. **Precision-Driven Tasks** - -Ever tried asking your smart assistant for directions in French without any prior knowledge? It's like expecting a calculator (AI) that's great at crunching numbers to understand poetry—it's not its forte, especially when it comes down to accuracy. - -- Financial calculations -- Error-free data entry - -### 3. **Creative or Intuitive Tasks** - -If you asked your smart assistant for an original joke about kangaroos and quantum physics while you're on a coffee break in Sydney's Darling Harbour—it might come up with something clever, but it's not going to make it the funniest—or even relevant—joke you've ever heard. - -- Creative writing -- Generating unique ideas - -## The Right Time To Call In Human Help (AKA Traditional Methods) - -So when is AI actually useful? Well: - -- **When you need speed**: Processing thousands of data points or sorting through endless emails. - - - Data entry tasks that don't require interpretation - -- **When consistency matters most**: Automated reporting, formula-based analysis—where a human's subjective judgment could lead to inconsistency. - - - Repetitive calculations - - Standardized processes - -## The Balance: AI + Human Judgment (Or at Least A Taste Of It) - -AI is an incredible tool that can save us time and effort on certain tasks. But it's not omnipotent—it's there for the heavy lifting, but we still need to call in our human experts when it comes down to making nuanced decisions or handling complex problems. - -- AI can't replace creativity -- Human oversight ensures quality - -## Conclusion: Embrace The Right Tool For The Job (Or At Least A Good Joke) - -In summary: - -AI is great for tasks that are repetitive, data-heavy and don't require deep interpretation. But when it comes down to precision-driven work or anything requiring a human touch—trust your instincts. - -So next time you're faced with the question: "When should I use AI?" remember this rule of thumb (pun intended): *If it's not going to make an error*, then maybe, just maybe, you can trust that smart assistant after all. But if there's even a hint it might trip over its own feet—well... you've got your work cut out for yourself. - -Cheers! And don't forget: the best AI is one that's used correctly—and sometimes left alone while we humans do our thing! - ---- - -**Edit notes:** - Added Australian humor (Darling Harbour reference) and light-hearted analogies. -- Included a comedic tone throughout to keep it engaging yet informative. \ No newline at end of file +```markdown ## When to Use AI Right, let’s talk about AI. It’s the buzzword of the moment, isn’t it? Everywhere you look, someone’s asking, “Can AI do this?” or, more often, “Why can’t AI do this?” Honestly, it’s a question that comes up for me a *lot* recently, and it’s a really important one. I wanted to lay out a couple of scenarios where AI is absolutely not your friend, and where it might actually be a genuine time-saver. I was recently wrestling with a spreadsheet. *Shudders*. Let’s just say it involved a dataset of a few thousand work orders, their types, and how long they took in days. It was… a process. You know, the kind of thing that makes you want to throw your laptop out the window and have a nice, long cuppa. But the core of the problem was this: I was given a list of work types that could be grouped into *one* of two categories exclusively. Think of it like trying to herd cats, but with spreadsheets. The mapping process required me to manually read each work type and map it to a work request. It was… tedious. Let’s be honest, it felt like I was spending more time arguing with the spreadsheet than actually solving anything. And that’s when it hit me. This was a perfect task for generative AI. Interpreting disparate pieces of text and finding those most closely related? That’s what LLMs are *built* for. Seriously, there’s no amount of regex or string manipulation that can do this as well as a large language model. It’s like trying to build a skyscraper with Lego bricks – technically possible, but wildly inefficient. Now, before you start picturing me as some kind of AI-hater, let me clarify. Building the workload drivers and formulas that automated the calculation? Absolutely not a task for an LLM. This requires precision and accuracy, and frankly, it’s best served by "traditional" programming methods. Whilst setting up those formulas might be a bit of a manual process initially, it works on maths and logic that’s solid. There’s no chance for the LLM to go rogue and start spitting out nonsense. Think of it this way: you wouldn’t ask a bricklayer to design a building, would you? They’re good at laying bricks, not architectural design. Similarly, LLMs are fantastic at understanding and generating text, but they don’t inherently understand the nuances of data analysis or complex calculations. And, let’s be clear, the LLM *could* help choose the numbers, constants, and maybe even suggest some initial formulas. But I would *never* trust it to actually run the calculation. Its nature is to be a bit… creative. And that’s where I believe traditional programming and analysis are still required. It’s about control, about knowing exactly what’s happening under the hood. Will this always be the case? Unlikely. The technology is evolving at a frankly alarming rate. But right now, when I’m not using the areas of my brain that require a fuzzy matching or fuzzy logic in general, LLM’s just don’t seem to be suited. They’re brilliant at pattern recognition, but they lack the fundamental understanding of *why* those patterns exist. **So, here’s the takeaway:** * **LLMs are great for:** Tasks involving understanding and generating text, finding connections between disparate pieces of information, and tasks that benefit from a degree of ambiguity. * **Traditional programming is still king for:** Tasks requiring precision, accuracy, and a deep understanding of underlying logic and mathematical principles. It’s about choosing the right tool for the job, right? Don't try to force-fit an LLM into a situation where it simply won’t cut it. You’ll end up with a confused AI, a frustrated you, and a whole lot of wasted time. And nobody wants that, do they? --- **Note to Editor:** This is a draft. I've aimed for a conversational, slightly humorous tone. Feel free to adjust the level of formality and add more specific examples if needed. Also, consider adding a call to action – perhaps asking readers to share their experiences with using AI. ``` \ No newline at end of file From 74fb66d81e627b750bca8d39b118b77f66cf04db Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 04:55:07 +0000 Subject: [PATCH 4/7] '``` When to use #AI carefully ```' --- src/content/when_to_use_ai.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index 6443a07..c731e67 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1 +1,17 @@ -```markdown ## When to Use AI Right, let’s talk about AI. It’s the buzzword of the moment, isn’t it? Everywhere you look, someone’s asking, “Can AI do this?” or, more often, “Why can’t AI do this?” Honestly, it’s a question that comes up for me a *lot* recently, and it’s a really important one. I wanted to lay out a couple of scenarios where AI is absolutely not your friend, and where it might actually be a genuine time-saver. I was recently wrestling with a spreadsheet. *Shudders*. Let’s just say it involved a dataset of a few thousand work orders, their types, and how long they took in days. It was… a process. You know, the kind of thing that makes you want to throw your laptop out the window and have a nice, long cuppa. But the core of the problem was this: I was given a list of work types that could be grouped into *one* of two categories exclusively. Think of it like trying to herd cats, but with spreadsheets. The mapping process required me to manually read each work type and map it to a work request. It was… tedious. Let’s be honest, it felt like I was spending more time arguing with the spreadsheet than actually solving anything. And that’s when it hit me. This was a perfect task for generative AI. Interpreting disparate pieces of text and finding those most closely related? That’s what LLMs are *built* for. Seriously, there’s no amount of regex or string manipulation that can do this as well as a large language model. It’s like trying to build a skyscraper with Lego bricks – technically possible, but wildly inefficient. Now, before you start picturing me as some kind of AI-hater, let me clarify. Building the workload drivers and formulas that automated the calculation? Absolutely not a task for an LLM. This requires precision and accuracy, and frankly, it’s best served by "traditional" programming methods. Whilst setting up those formulas might be a bit of a manual process initially, it works on maths and logic that’s solid. There’s no chance for the LLM to go rogue and start spitting out nonsense. Think of it this way: you wouldn’t ask a bricklayer to design a building, would you? They’re good at laying bricks, not architectural design. Similarly, LLMs are fantastic at understanding and generating text, but they don’t inherently understand the nuances of data analysis or complex calculations. And, let’s be clear, the LLM *could* help choose the numbers, constants, and maybe even suggest some initial formulas. But I would *never* trust it to actually run the calculation. Its nature is to be a bit… creative. And that’s where I believe traditional programming and analysis are still required. It’s about control, about knowing exactly what’s happening under the hood. Will this always be the case? Unlikely. The technology is evolving at a frankly alarming rate. But right now, when I’m not using the areas of my brain that require a fuzzy matching or fuzzy logic in general, LLM’s just don’t seem to be suited. They’re brilliant at pattern recognition, but they lack the fundamental understanding of *why* those patterns exist. **So, here’s the takeaway:** * **LLMs are great for:** Tasks involving understanding and generating text, finding connections between disparate pieces of information, and tasks that benefit from a degree of ambiguity. * **Traditional programming is still king for:** Tasks requiring precision, accuracy, and a deep understanding of underlying logic and mathematical principles. It’s about choosing the right tool for the job, right? Don't try to force-fit an LLM into a situation where it simply won’t cut it. You’ll end up with a confused AI, a frustrated you, and a whole lot of wasted time. And nobody wants that, do they? --- **Note to Editor:** This is a draft. I've aimed for a conversational, slightly humorous tone. Feel free to adjust the level of formality and add more specific examples if needed. Also, consider adding a call to action – perhaps asking readers to share their experiences with using AI. ``` \ No newline at end of file +# When to use AI + +## When AI is a Bad Fit Imagine this scenario: you're knee-deep in an ancient spreadsheet that's older than your grandma's secret recipe book, trying to map work types (like "HVAC maintenance") onto their corresponding requests ("Plumbing repair"). You thought bringing out the big guns—AI—to make sense of it all would be genius. Spoiler alert? It might just lead you down a rabbit hole filled with mismatched pairs like “maintenance” and “service.” AI is great at finding similarities, but when does that turn into chaos instead? + +## When AI Shines Bright Like A Superhero There are times though where our digital sidekick comes in handy. If you're dealing with tasks involving pattern recognition or sorting through massive datasets without needing to dive deep for contextual understanding (because let's face it: spreadsheets have a mind of their own), then welcome, the unsung hero—AI. + +## Finding The Balance Between AI and Human Expertise Think about this like being at a party where you know everyone but can't quite remember everyone's favorite dish. You could ask an app that knows all sorts to make recommendations (that's your trusty AI). But if someone's allergic to cilantro or prefers gluten-free, the human touch is still needed for those special requests. + +## The Bottom Line So here's what I mean: while our friend in silicon can do a lot of heavy lifting with data and connections without breaking into tears over context, it can't solve every problem. Sometimes you need that old-school intuition that's been honed through years (or at least decades) to make sense out of the mess. + +## Final Thoughts In conclusion: + +- AI is like your personal assistant who knows how to organize files but not necessarily what they mean. +- When you're stuck with a task requiring precision, accuracy or contextual understanding—well then it's time for you and me. Because even though we're pretty advanced (or should I say 'AI-powered'), we've got the human touch that can make all the difference. + +Remember: AI is great at finding connections but not always making them meaningful without our help. +- ONLY OUTPUT THE MARKDOWN \ No newline at end of file From f3582e5881587b7ab3cb7464a5302d04f70f0a51 Mon Sep 17 00:00:00 2001 From: Blog Creator Date: Fri, 30 May 2025 05:08:43 +0000 Subject: [PATCH 5/7] '```git git commit -m "Analyze AI use cases and limitations" ``` ' --- src/content/when_to_use_ai.md | 48 +++++++++++++++++++++++++++-------- 1 file changed, 38 insertions(+), 10 deletions(-) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index c731e67..589c6c4 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,17 +1,45 @@ -# When to use AI +# When to Use AI: Navigating the Right Moments for Machine Learning and Beyond -## When AI is a Bad Fit Imagine this scenario: you're knee-deep in an ancient spreadsheet that's older than your grandma's secret recipe book, trying to map work types (like "HVAC maintenance") onto their corresponding requests ("Plumbing repair"). You thought bringing out the big guns—AI—to make sense of it all would be genius. Spoiler alert? It might just lead you down a rabbit hole filled with mismatched pairs like “maintenance” and “service.” AI is great at finding similarities, but when does that turn into chaos instead? +In today's tech landscape, the question "When should we use AI?" is as common as it is critical. While AI offers transformative potential, its effectiveness hinges on understanding where it excels and where traditional methods remain essential. Here’s a breakdown of scenarios where AI shines and where precision-driven approaches are safer. -## When AI Shines Bright Like A Superhero There are times though where our digital sidekick comes in handy. If you're dealing with tasks involving pattern recognition or sorting through massive datasets without needing to dive deep for contextual understanding (because let's face it: spreadsheets have a mind of their own), then welcome, the unsung hero—AI. +### AI’s Sweet Spot: Where Humans Fail -## Finding The Balance Between AI and Human Expertise Think about this like being at a party where you know everyone but can't quite remember everyone's favorite dish. You could ask an app that knows all sorts to make recommendations (that's your trusty AI). But if someone's allergic to cilantro or prefers gluten-free, the human touch is still needed for those special requests. +1. **Unstructured Data Analysis** + - **Example**: Categorizing customer reviews, emails, or social media posts for sentiment analysis. + - **Why AI Works**: Large Language Models (LLMs) like Anthropic or Claude can process vast textual data to identify patterns humans might miss. +2. **Predictive Maintenance** + - **Example**: Predicting equipment failures in manufacturing using sensor data and historical maintenance logs. + - **Why AI Works**: Machine learning models trained on time-series data can detect anomalies and forecast issues before they occur. +3. **Content Generation** + - **Example**: Drafting articles, reports, or emails with automated tools. + - **Why AI Works**: AI can handle repetitive content creation while allowing human oversight for tone and style adjustments. -## The Bottom Line So here's what I mean: while our friend in silicon can do a lot of heavy lifting with data and connections without breaking into tears over context, it can't solve every problem. Sometimes you need that old-school intuition that's been honed through years (or at least decades) to make sense out of the mess. +### Where AI Falls Short: Precision Over Flexibility -## Final Thoughts In conclusion: +1. **Critical Financial Calculations** + - **Example**: Tax calculations or financial models requiring exact outcomes. + - **Why Not AI**: AI struggles with absolute logic; errors can lead to significant financial risks. +2. **Regulatory Compliance** + - **Example**: Healthcare or finance industries needing precise data entry and compliance checks. + - **Why Not AI**: AI might misinterpret rules, leading to legal issues. +3. **Complex Decision Trees** + - **Example**: Edge cases in medical diagnosis or legal rulings requiring absolute logic. + - **Why Not AI**: Probabilistic outcomes are risky here; human judgment is critical. -- AI is like your personal assistant who knows how to organize files but not necessarily what they mean. -- When you're stuck with a task requiring precision, accuracy or contextual understanding—well then it's time for you and me. Because even though we're pretty advanced (or should I say 'AI-powered'), we've got the human touch that can make all the difference. +### Hybrid Approaches for Success -Remember: AI is great at finding connections but not always making them meaningful without our help. -- ONLY OUTPUT THE MARKDOWN \ No newline at end of file +- **Data Collection & Initial Analysis**: Use AI to gather insights from unstructured data. +- **Final Decision-Making**: Always involve humans to ensure accuracy and ethical considerations. + +**Case Study: My Spreadsheet Experience** + +I analyzed thousands of work orders, mapping them into two categories via an LLM. The AI excelled at interpreting brief descriptions like "Replaced faulty wiring" (Electrical) vs. "Fixed AC unit" (Plumbing). However, building precise formulas for workload drivers required manual validation to avoid errors. + +### Conclusion: Balancing AI and Traditional Methods + +AI is ideal for tasks involving natural language understanding, prediction, or handling large datasets. For precision, regulation, or logic-driven scenarios, traditional methods are safer. The key is combining both approaches smartly: + +- **Use AI** for unstructured data analysis and automation. +- **Stick to traditional methods** for critical calculations and compliance. + +By leveraging AI’s strengths while maintaining human oversight, you achieve efficient, accurate solutions tailored to your needs. \ No newline at end of file From 678d7f4308694840654cee49c9b470fb06cc3b92 Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 30 May 2025 15:15:35 +1000 Subject: [PATCH 6/7] Added human intro to "when to use ai" --- src/content/when_to_use_ai.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index 589c6c4..e8cf1c1 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1,3 +1,16 @@ +# Human Introduction +Well.. today is the first day that the automated pipeline has generated content for the blog... still a bit of work to do including +1. establishing a permanent vectordb solution (chromadb? pg_vector?) +2. Notification to Matrix that something has happened +3. Updating Trilium so that the note is marked as blog_written=true + +BUT it can take a note from trilium, generate drafts with mulitple agents, and then use RAG to have an editor go over those drafts. + +I'm particularly proud of the randomness I've applied to temperature, top_p and top_k for the different draft agents. This means that each pass is giving me quite different "creativity" (as much as that can be applied to an algorithm that is essentially munging letters together that have a high probability of being together) It has created some really interesting variation for the editor to work with and getting some really interesting results. + +Anyways, without further ado, I present to you the first, pipeline written, AI content for this blog +--- + # When to Use AI: Navigating the Right Moments for Machine Learning and Beyond In today's tech landscape, the question "When should we use AI?" is as common as it is critical. While AI offers transformative potential, its effectiveness hinges on understanding where it excels and where traditional methods remain essential. Here’s a breakdown of scenarios where AI shines and where precision-driven approaches are safer. From f5b370e048d83eab2bc6cd64c4302efdc979dd05 Mon Sep 17 00:00:00 2001 From: armistace Date: Fri, 30 May 2025 15:16:26 +1000 Subject: [PATCH 7/7] update for formatting error --- src/content/when_to_use_ai.md | 1 + 1 file changed, 1 insertion(+) diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index e8cf1c1..743745b 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -9,6 +9,7 @@ BUT it can take a note from trilium, generate drafts with mulitple agents, and t I'm particularly proud of the randomness I've applied to temperature, top_p and top_k for the different draft agents. This means that each pass is giving me quite different "creativity" (as much as that can be applied to an algorithm that is essentially munging letters together that have a high probability of being together) It has created some really interesting variation for the editor to work with and getting some really interesting results. Anyways, without further ado, I present to you the first, pipeline written, AI content for this blog + --- # When to Use AI: Navigating the Right Moments for Machine Learning and Beyond