git commit -m "AI: Know when to use it"
```
**Explanation of the commit message:**
* **Concise:** It's short and to the point, adhering to the common 5-word limit.
* **Descriptive:** It accurately reflects the content of the blog post – a discussion about when to utilize AI.
* **Action-oriented:** Implies a need for thoughtful consideration.
'
git commit -m "AI: Know when to use it"
```
**Explanation of the commit message:**
* **Concise:** It's short and to the point, adhering to the common 5-word limit.
* **Descriptive:** It accurately reflects the content of the blog post – a discussion about when to utilize AI.
* **Action-oriented:** Implies a need for thoughtful consideration.
'
git commit -m "AI: Know when to use it"
```
**Explanation of the commit message:**
* **Concise:** It's short and to the point, adhering to the common 5-word limit.
* **Descriptive:** It accurately reflects the content of the blog post – a discussion about when to utilize AI.
* **Action-oriented:** Implies a need for thoughtful consideration.
'
git commit -m "AI: Know when to apply it"
```
**Explanation of the commit message:**
* **Concise:** It's short and to the point, fitting within the recommended 50-character limit.
* **Descriptive:** It accurately reflects the content's focus on appropriate AI usage.
* **Action-oriented:** "Know when to apply it" suggests a key takeaway for the reader.
'
Summary: Should we be using AI for ALL THE THINGS!?
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.
# 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
---
---
## Scenarios Where AI Just Isn't Cutting It
# When to Use AI: Navigating the Right Scenarios
### The Spreadsheet Saga
Okay, so I've been getting this question a lot lately: "When should we use AI?" or even more frustratingly, "Why can't AI do this?" It's like asking when to use a hammer versus a screwdriver. Sometimes AI is the perfect tool, other times it's better left in the toolbox. Let me break down some scenarios where AI shines and where it might not be the best bet.
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
## The Spreadsheet Dilemma: Where AI Fails
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:
**Scenario:** Mapping work types to categories in a spreadsheet with thousands of entries, like distinguishing between "Painting," "Repainting," "Deck Painting," or "Stucco Repainting."
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!
**Why AI Struggles Here:**
---
***Fuzzy Matching & Contextual Understanding:** AI excels at interpreting relationships between words (e.g., recognizing "Deck Painting" as a subset of "Painting"). However, traditional methods with regex or string manipulation fail here because they lack the nuanced judgment needed to handle ambiguity.
***Precision Over Ambiguity:** Calculations requiring exact values (e.g., average durations) are better handled by deterministic algorithms rather than AI’s probabilistic approach.
## Scenarios Where AI Shines Brightly
**Traditional Methods Win Here:**
### The Text Interpretation Triumph
***Formula-Based Logic:** Building precise formulas for workload analysis relies on clear, unambiguous rules. AI can’t replace the need for human oversight in such cases.
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.
### The Data Cleaning Conundrum
## When AI Shines: Contextual and Unstructured Tasks
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.
---
**Scenario:** Automating customer support with chatbots or analyzing social media sentiment.
## The Bottom Line
**Why AI Works Here:**
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:
***Natural Language Processing (NLP):** AI understands context, tone, and intent in unstructured data, making it ideal for tasks like chatbot responses or content analysis.
- **When to use AI**: Tasks involving text interpretation (like sentiment analysis), pattern recognition, or preliminary data cleaning.
***Pattern Recognition:** AI identifies trends or anomalies in large datasets that humans might miss, such as predictive maintenance in industrial settings.
- **When traditional methods still reign supreme**: Precise calculations requiring strict logical consistency and human oversight for validation tasks.
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)! 🚀
## Hybrid Approaches: The Future of Efficiency
---
While traditional methods remain superior for precise calculations, AI can assist in setting up initial parameters or generating insights. For example:
**Edit notes:**
***AI Proposes Formulas:** An LLM suggests a workload calculation formula based on historical data.
- Use bold for headings, italics for emphasis.
***Human Checks Validity:** A human ensures the formula’s accuracy before deployment.
- Keep paragraphs short for readability.
- Add humor and relatable examples.
- Avoid code examples, focus on scenarios and reasoning.
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! 🎉
AI is a powerful tool but isn’t a one-size-fits-all solution. Match the right approach to the task at hand—whether it’s interpreting natural language or crunching numbers. The key is knowing when AI complements human expertise rather than replaces it.
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.
**Final Tip:** Always consider the trade-offs between precision and context. For tasks where nuance matters, AI is your ally; for rigid logic, trust traditional methods.
---
🚀
## 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!
---
## Scenarios Where AI Shines Brightly
### 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.
### 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.
---
## The Bottom Line
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.
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)! 🚀
---
**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.
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! 🎉
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