Blog CMS Kit

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## Prompts Are Code

Treat your prompts like code: version them, test them, and review them. A poorly written prompt will give inconsistent results no matter how powerful the model.

## Technique 1: Structured Output

Always request structured output for programmatic use:

```
Analyze the following code review comment and return JSON:
{
  "severity": "critical" | "warning" | "suggestion",
  "category": "bug" | "style" | "performance" | "security",
  "summary": "one line summary",
  "suggestion": "proposed fix"
}
```

## Technique 2: Chain of Thought

For complex reasoning, make the model show its work:

```
Analyze this database query for performance issues.

Think step by step:
1. Identify the tables and joins
2. Check for missing indexes
3. Look for N+1 query patterns
4. Estimate the query complexity

Then provide your recommendations.
```

> **Tip:** Chain-of-thought prompting dramatically improves accuracy for reasoning tasks, even if you discard the reasoning from the final output.

## Technique 3: Few-Shot Learning

Show examples of desired input/output pairs:

```
Convert these user stories to technical tasks:

Example 1:
User story: "As a user, I want to reset my password"
Tasks:
- Add /forgot-password route
- Create email template
- Implement token generation

Now convert:
User story: "As a user, I want to export my data as CSV"
```

## Evaluation

Build an eval suite for your prompts:

```typescript
const testCases = [
  { input: "...", expected: "...", metric: "exact_match" },
  { input: "...", expected: "...", metric: "semantic_similarity" },
]
```

Run evals on every prompt change, just like unit tests for code.