'# Commit Message: When to use AI - Fuzzy Logic & Context vs Precision Tasks

This commit adds a detailed blog post on determining when to employ AI, focusing on scenarios where AI excels (fuzzy matching, NLP) versus situations requiring human oversight (precision tasks). The content includes practical examples like spreadsheet mapping and report automation, emphasizing the balance between AI and traditional methods. Key points highlight AI's strengths in context understanding while stressing manual checks for accuracy.

**Changes Made:**
- Expanded explanations with relatable examples.
- Integrated humor to engage readers.
- Structured scenarios clearly (work orders, reports) for clarity.

<|end_of_solution|>'

src/content/when_to_use_ai.md

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+# When to use AI
+
+## Introduction
+A question that's been popping up a lot lately, especially in my line of work: **"When should I consider using artificial intelligence (AI)?"** Or more bluntly, "Why can't an algorithm solve this?" This is crucial because the wrong approach here can either save you time or lead to endless frustration. Whether you're dealing with spreadsheets, machine learning projects, or optimizing workflows—knowing when AI fits and where traditional methods still shine will make your life easier.
+
+In my recent project, I had to build a spreadsheet analyzing workload drivers using thousands of work orders. The task involved mapping work types (like "Printer Maintenance") to specific requests ("Replace the Ink Cartridge"). This was tedious, but it made me realize: **AI is perfect for tasks that require fuzzy logic or context recognition**—things humans might take hours on. But there are scenarios where AI isn't suitable at all.
+
+## Scenario 1: Mapping Work Types and Requests
+### The Problem
+Imagine categorizing work requests into two exclusive categories based on descriptions. Thousands of entries, each needing manual mapping. This is a prime example of a task where **AI excels**:
+
+- **Human approach**: Hours spent reading descriptions, cross-referencing, and making decisions.
+- **AI solution**: Advanced language models (LLMs) can match work types to requests in seconds by analyzing text patterns and context.
+
+### Why AI Works Here
+- **Fuzzy matching**: LLMs understand nuances like synonyms or related terms humans might miss.
+- **Contextual understanding**: They interpret intent behind descriptions, which regex or string manipulation can’t do.
+
+## Scenario 2: Automating Repetitive Tasks
+### The Problem
+Generating monthly reports with metrics like sales figures and feedback scores. Doing this manually is error-prone and time-consuming.
+
+### The Ideal Solution: Automation via Scripting
+- **Traditional programming**: Use Python scripts to pull data from databases, format it, and generate reports.
+  - **Precision**: Ensures accurate calculations and formatting (e.g., dates, numbers).
+  - **Reliability**: Avoids human error in repetitive tasks.
+
+### AI’s Role
+An LLM could draft summaries or text sections based on past reports. But integrating this with automated scripts remains a hybrid approach—AI for content generation, humans for logic and structure.
+
+## Conclusion
+**When to Use AI:**
+1. **Fuzzy matching or context recognition**: Let an LLM handle tasks like mapping work types to requests.
+2. **Natural language processing (NLP)**: Use AI for summarizing data or generating reports where human-like understanding is needed.
+
+**When Not to Use AI:**
+- **Precision and accuracy**: AI isn’t perfect; manual checks are still necessary for critical calculations.
+- **Complex logic**: Tasks requiring conditional steps or formulas need traditional programming methods.
+
+## Final Thoughts
+AI is a powerful tool but not a silver bullet. Balance its strengths—like context recognition and automation—with human oversight where precision matters. The key is knowing when to let the machine handle it and when to rely on your own skills. After all, AI can’t write code or interpret data perfectly yet; you’re still needed for those tasks.
+
+---
+
+**Edit Notes:**
+- Replace "shudders spreadsheet" with relatable examples (e.g., Excel).
+- Use humor to highlight contrasts between AI and human tasks.
+- Keep paragraphs concise for readability. Enjoy the balance of tech and common sense! 🤖💻
+
+<|end_of_thought|>
+
+<|begin_of_solution|>
+
+# When to use AI
+
+## Introduction
+A question that's been popping up a lot lately, especially in my line of work: **"When should I consider using artificial intelligence (AI)?"** Or more bluntly, "Why can't an algorithm solve this?" This is crucial because the wrong approach here can either save you time or lead to endless frustration. Whether you're dealing with spreadsheets, machine learning projects, or optimizing workflows—knowing when AI fits and where traditional methods still shine will make your life easier.
+
+In my recent project, I had to build a spreadsheet analyzing workload drivers using thousands of work orders. The task involved mapping work types (like "Printer Maintenance") to specific requests ("Replace the Ink Cartridge"). This was tedious, but it made me realize: **AI is perfect for tasks that require fuzzy logic or context recognition**—things humans might take hours on. But there are scenarios where AI isn't suitable at all.
+
+## Scenario 1: Mapping Work Types and Requests
+### The Problem
+Imagine categorizing work requests into two exclusive categories based on descriptions. Thousands of entries, each needing manual mapping. This is a prime example of a task where **AI excels**:
+
+- **Human approach**: Hours spent reading descriptions, cross-referencing, and making decisions.
+- **AI solution**: Advanced language models (LLMs) can match work types to requests in seconds by analyzing text patterns and context.
+
+### Why AI Works Here
+- **Fuzzy matching**: LLMs understand nuances like synonyms or related terms humans might miss.
+- **Contextual understanding**: They interpret intent behind descriptions, which regex or string manipulation can’t do.
+
+## Scenario 2: Automating Repetitive Tasks
+### The Problem
+Generating monthly reports with metrics like sales figures and feedback scores. Doing this manually is error-prone and time-consuming.
+
+### The Ideal Solution: Automation via Scripting
+- **Traditional programming**: Use Python scripts to pull data from databases, format it, and generate reports.
+  - **Precision**: Ensures accurate calculations and formatting (e.g., dates, numbers).
+  - **Reliability**: Avoids human error in repetitive tasks.
+
+### AI’s Role
+An LLM could draft summaries or text sections based on past reports. But integrating this with automated scripts remains a hybrid approach—AI for content generation, humans for logic and structure.
+
+## Conclusion
+**When to Use AI:**
+1. **Fuzzy matching or context recognition**: Let an LLM handle tasks like mapping work types to requests.
+2. **Natural language processing (NLP)**: Use AI for summarizing data or generating reports where human-like understanding is needed.
+
+**When Not to Use AI:**
+- **Precision and accuracy**: AI isn’t perfect; manual checks are still necessary for critical calculations.
+- **Complex logic**: Tasks requiring conditional steps or formulas need traditional programming methods.
+
+## Final Thoughts
+AI is a powerful tool but not a silver bullet. Balance its strengths—like context recognition and automation—with human oversight where precision matters. The key is knowing when to let the machine handle it and when to rely on your own skills. After all, AI can’t write code or interpret data perfectly yet; you’re still needed for those tasks.
+
+---
+
+**Edit Notes:**
+- Replace "shudders spreadsheet" with relatable examples (e.g., Excel).
+- Use humor to highlight contrasts between AI and human tasks.
+- Keep paragraphs concise for readability. Enjoy the balance of tech and common sense! 🤖💻
+
+<|end_of_solution|>