Analysis
Seeing the signal in the noise.
Humans are good at intuitive leaps. AI is good at processing massive volume without fatigue. Together, you solve the data overflow problem.
The Analysis Matrix
| Capability | Prompt Pattern | Outcome |
|---|---|---|
| Summarize | "TL;DR this in 3 bullet points focusing on risks" | Speed |
| Extract | "Turn this messy text into a JSON object with keys: Name, Date, Action" | Structure |
| Classify | "Label each support ticket: Urgent, Bug, Feature Request" | Order |
| Pattern Match | "Find the contradiction between Document A and Document B" | Insight |
Techniques
1. Few-Shot Prompting
Don't just explain; show.
"Extract company names. Input: 'I bought an iPhone from Apple yesterday.' -> Output: Apple Input: 'Microsoft released a new update.' -> Output: Microsoft Input: '[Your text here]' -> Output: "
2. Chain of Thought
Force the model to show its work. This reduces logic errors in complex analysis.
"Analyze the financial health of this company based on the text. Think step-by-step. First, identify revenue trends. Second, check debt levels. Finally, provide a verdict."
3. The Skeptic Persona
AI tends to be agreeable. Force it to be critical.
"You are a ruthless auditor. Find every logical fallacy, unproven assumption, and weak correlation in this argument."
4. Format Enforcement
Data is useless if you can't pipe it.
"Output ONLY raw CSV. No markdown, no intro text, no explanations."
Context
- Pattern Recognition - The underlying human skill.
- DeepSeek R1 - Excellent for complex analytical reasoning.
- Gemini - Best for large context windows (analyzing whole books/codebases).
Data is not information. Information is not knowledge. Knowledge is not wisdom.