diff --git a/src/content/when_to_use_ai.md b/src/content/when_to_use_ai.md index 4524547..b52f1bd 100644 --- a/src/content/when_to_use_ai.md +++ b/src/content/when_to_use_ai.md @@ -1 +1,94 @@ -```markdown # When to use AI As an Australian software developer, DevOps expert and journalist who loves diving into tech topics with humor sprinkled throughout my writing (because why not?), I've found myself pondering this question quite often: "When should I consider using artificial intelligence?" Well, buckle up because I'm about to share a few scenarios where the answer is pretty clear-cut. ## When AI Just Isn't Right ### The Shuddering Spreadsheet Saga Recently, while working on an almost terrifying spreadsheet involving workload drivers and potential savings within various processes (shudders), I've stumbled upon something interesting. I had this massive dataset of work orders that needed to be categorized into one or two exclusive types based solely off the description provided. #### Human vs AI: A Tangential Tango As part of my task, it became evident how closely related these "work types" and actual requests were — they could have been more tangentially connected than a kangaroo in high heels. The mapping process required me to manually read each work type against the request description. Now here's where I thought I'd enlist some help from Gen AI (Generative Pre-trained Transformer, for those who love acronyms). This was essentially an exercise of interpreting disparate pieces of text and finding their most closely related counterparts — a task that could easily be handled by LLMs designed to find intersections between separate texts. No amount of regex or string manipulation can match the precision required here. #### Traditional Programming: The Unsung Hero But wait, there's more! Building those workload drivers and formulas for automated calculations? That's where traditional programming methods come into play — with their impeccable math skills (and zero chance for interpretation). While LLMs might assist in choosing numbers or constants initially, I wouldn't trust them to actually run the calculation. Their nature could potentially lead to some funky results. In conclusion: When it comes down to tasks requiring precision and accuracy rooted deeply within mathematics and logic — that's when traditional programming shines brighter than an Australian summer day (or even a Gen AI model). ## But Sometimes... It's Perfectly Okay ### The Workload Drivers Dream Team On the flip side, let's consider scenarios where LLMs can truly shine. Take my spreadsheet example again: categorizing work orders based on descriptions is perfectly suited for Generative Pre-trained Transformers. #### Finding Intersections with Ease (and Humor) Gen AI models excel at interpreting text and finding relationships between disparate pieces of information — something that would otherwise be a laborious human task or an impossible feat without extensive regex magic. In this context, Gen AI can effortlessly match work types to requests by analyzing descriptions in ways humans can't possibly fathom. #### Traditional Programming: Still the Ace Up Your Sleeve However, even when leveraging LLMs for categorization and intersection-finding tasks within a spreadsheet-like environment (because why not), traditional programming still holds its ground. It’s essential because it ensures that once we’ve categorized our work orders using Gen AI's brilliance — we can run those calculations with precision through the trusty old-school methods. In conclusion: When dealing with text interpretation, categorization based on descriptions or any task requiring a deep dive into language nuances and fuzzy logic (because why not), LLMs are your go-to. But when it comes to executing precise mathematical operations rooted in traditional programming principles — that's where we still reign supreme! ## So What's the Verdict? In essence: Use AI wisely, folks! Whether it's interpreting texts or performing intricate calculations grounded firmly within a structured logical framework (because who needs fuzzy logic anyway?), knowing which tool fits best for each task is crucial. And hey, if you ever find yourself in an Australian summer debating whether to use Gen AI versus traditional programming methods — just remember that both have their unique strengths and quirks. So go forth with your tech adventures confidently! Whether you're a software developer crafting the next groundbreaking app or merely enjoying this delightful journey through technology's endless possibilities (with a hint of humor, naturally), always know when it's time to let Gen AI take over... and sometimes even more importantly — knowing when not to. ``` +# When to use AI + +## The Great AI Debate: When to Trust the Machine vs. When to Stick to Your Brain + +As a journalist, software developer, and DevOps expert, I’ve spent years navigating the murky waters of AI’s “what if” scenarios. The question that keeps coming up professionally is: *“When to use AI?”* And the answer, like many things in life, is *“It depends.”* But when I’m not using my brain, I’m using my shudders. Let’s dive into the scenarios where AI isn’s the best choice—and where it *is* the best choice. + +## When AI is Not the Best Choice + +### 1. **The Fuzzy Logic Dilemma** + +AI is great at handling *fuzzy logic*—like when you’re trying to figure out if a work order is “urgent” or “not urgent” based on vague descriptions. But when you’re trying to map a work type to a specific work request, AI is *not* the best choice. Imagine this: You have a dataset of work orders, each with a type (e.g., “installation,” “repair,” “maintenance”) and a duration in days. You’re trying to group these into two categories: *“high-priority”* and *“low-priority.”* The problem? The work types and work requests are *tangentially related*. + +> *“I was given a list of work types that could be grouped into 1 of 2 categories exclusively.”* +> *“The problem was… the ‘work types’ and work requests were at best tangentially related.”* + +So I had to manually read each work type and map it to a work request. It was a *shudder-inducing* task. AI, on the other hand, would have *automated* that. But it would also have *misunderstood* the relationship between the two. + +### 2. **The Math and Logic Job** + +AI is great at math and logic. If you need to calculate the total time spent on a process, or create a formula that accounts for variables like labor hours, weather, or equipment downtime, AI can handle it. For example, I once built a spreadsheet that calculated potential savings in a process. The formulas were precise, and the logic was sound. + +> *“Traditional programming methods work on maths and logic that is sound and offers no chance for interpretation.”* + +So even if AI could suggest the numbers, I’d never trust it to actually run the calculation. It’s like trusting a robot to drive a car when it’s not built for that. + +## When AI is the Best Choice + +### 1. **Finding Connections** + +AI excels at identifying patterns and relationships within large datasets. This is invaluable for tasks like customer segmentation, fraud detection, and predictive maintenance. + +### 2. **Automating Repetitive Tasks** + +Many tasks are simply too repetitive and time-consuming for humans to perform efficiently. AI-powered automation can free up valuable time and resources. + +### 3. **Suggesting Numbers or Constants** + +While AI shouldn't be trusted to run calculations, it can be helpful in suggesting potential numbers or constants to use in those calculations. + +## The Human vs. AI Divide + +AI is good at *finding connections* and *handling ambiguity*, but it’s *not good at precision*. When you need to ensure that a calculation is accurate, or that a mapping is flawless, AI is not the best choice. And that’s where humans shine. We’re good at *seeing* the connection between two things, even if it’s not obvious. We’re good at *precision* and *accuracy*. + +> *“I was given a list of work types that could be grouped into 1 of 2 categories exclusively.”* +> *“The problem was… the ‘work types’ and work requests were at best tangentially related.”* + +So I had to manually read each work type and map it to a work request. It was a *shudder-inducing* task. + +## The Final Thought + +So, in summary: + +* **AI is not the best choice** when you need precision, accuracy, or a human touch. +* **AI is the best choice** when you need to handle ambiguity, find connections, or automate repetitive tasks. + +And that’s why I’m a journalist, a developer, and a DevOps expert. I know when to trust the machine and when to rely on my brain. + +> *“When in doubt, just do it.”* + +## Editable Structure for Easy Customization + +### 📝 Title When to use AI + +### 🧠 Introduction + +* Briefly explain the question and the tone. + +### 🧩 Sections + +1. **When AI is Not the Best Choice** + * Subpoints: + * Fuzzy logic dilemmas + * Manual mapping tasks + * Precision vs. AI’s “fuzzy” logic +2. **When AI is the Best Choice** + * Subpoints: + * Math and logic tasks + * Automation of repetitive tasks + * Suggesting numbers or constants +3. **The Human vs. AI Divide** + * Highlight the importance of human judgment in critical tasks +4. **Final Thought** + * Encourage readers to think critically about when to use AI + +### 📝 Conclusion + +* Wrap up the key points and reinforce the message. + +## Tips for Editing + +* Replace the example about the spreadsheet with your own scenario. +* Add a personal anecdote or a relatable example. +* Use humor to keep the tone light and engaging. +* Add a call to action: “Next time you’re stuck, ask yourself: ‘Is this a task for AI or a task for me?’” \ No newline at end of file