src/content/when_to_use_ai.md

@@ -1,42 +1,59 @@
-# When to use AI 😄
-*A journalist, software developer, and DevOps expert’s take on when AI is overkill and when it’s just the right tool*
+# 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
 
-When I was building a spreadsheet called “shudders,” I was trying to figure out how to automate the process of mapping work types to work requests. The dataset was full of messy, unstructured text, and the goal was to find the best matches. At first, I thought, “This is a perfect use case for AI!” But then I realized: *this is the kind of problem where AI is basically a human’s worst nightmare*.
+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.
 
-So, let’s break it down.
+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.
 
-### 🧠 When AI is *not* the answer
+Anyways, without further ado, I present to you the first, pipeline written, AI content for this blog
 
-AI is great at pattern recognition, but it’s not great at *understanding context*. For example, if I had a list of work types like “customer service,” “technical support,” or “maintenance,” and I needed to map them to work requests that had vague descriptions like “this task took 3 days,” AI would struggle. It’s like trying to find a needle in a haystack—*but the haystack is made of human language*.
+---
 
-The problem with AI in this scenario is that it’s *not good at interpreting ambiguity*. If the work types are vague, the AI might mislabel them, leading to errors. Plus, when the data is messy, AI can’t keep up. I remember one time I tried to use a chatbot to classify work requests. It was so confused, it thought “customer service” was a type of “technical support.” 😅 The result? A spreadsheet full of “unknown” entries.
+# When to Use AI: Navigating the Right Moments for Machine Learning and Beyond
 
-### 🧮 When AI *is* the answer
+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.
 
-There are some scenarios where AI is *definitely* the way to go. For example, when you need to automate repetitive tasks, like calculating workloads or generating reports. These tasks are math-heavy and don’t require creative thinking. Let’s say you have a list of work orders, each with a start time, end time, and duration. You want to calculate the average time per task. AI can do that with precision. It’s like a calculator, but with a personality.
+### AI’s Sweet Spot: Where Humans Fail
 
-Another example: if you need to generate a report that summarizes key metrics, AI can handle that. It’s not about creativity, it’s about logic. And that’s where traditional programming shines.
+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 balance between AI and human oversight
+### Where AI Falls Short: Precision Over Flexibility
 
-AI is a tool, not a replacement for human judgment. While it can handle the *analyzing* part, the *decisions* still need to be made by humans. For instance, if you’re trying to decide which work type to assign to a request, AI might suggest “customer service” based on keywords, but the final decision depends on context.
+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.
 
-So, in the end, AI is a *helper*, not a *replacement*. It’s great for the parts that are repetitive, but the parts that require nuance, creativity, or deep understanding? That’s where humans step in.
+### Hybrid Approaches for Success
 
-### 🧩 Final thoughts
+- **Data Collection & Initial Analysis**: Use AI to gather insights from unstructured data.
+- **Final Decision-Making**: Always involve humans to ensure accuracy and ethical considerations.
 
-AI is like a superpower—great at certain things, not so great at others. It’s not a magic wand, but it’s a tool that can save time and reduce errors when used right.
+**Case Study: My Spreadsheet Experience**
 
-So, when is it time to say “AI, nope”? When the data is messy, the tasks are ambiguous, or the results need to be human-approved. And when is it time to say “AI, yes”? When you need to automate calculations, generate reports, or handle repetitive tasks that don’t require creativity.
+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.
 
-### 🧩 Summary
+### Conclusion: Balancing AI and Traditional Methods
 
-| Scenario | AI? | Reason |
-|---|---|---|
-| Ambiguous data | ❌ | AI struggles with context |
-| Repetitive tasks | ✅ | AI handles math and logic |
-| Creative decisions | ❌ | AI lacks the ability to think creatively |
+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:
 
-In the end, AI is just another tool. Use it when it works, and don’t let it define your workflow. 😄 *And if you ever feel like AI is overstepping, remember: it’s just trying to be helpful. Sometimes it’s not the best choice. Sometimes it’s the only choice.*
+- **Use AI** for unstructured data analysis and automation.
+- **Stick to traditional methods** for critical calculations and compliance.
 
-Let me know if you want a version with emojis or a table of contents! 🌟
+By leveraging AI’s strengths while maintaining human oversight, you achieve efficient, accurate solutions tailored to your needs.