'```git

git commit -m "Analyze AI use cases and limitations"
```
'

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.
+### Hybrid Approaches for Success
-- 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.
+- **Data Collection & Initial Analysis**: Use AI to gather insights from unstructured data.
-- ONLY OUTPUT THE MARKDOWN
+- **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.