dtsong/prompt-engineering

Prompt Engineering

Purpose

Design, evaluate, and version system prompts for LLM-powered features, including instruction structure, chain-of-thought patterns, output format constraints, and few-shot example selection.

Scope Constraints

Reads feature requirements, data format examples, and quality constraints for prompt design analysis. Does not execute LLM calls, modify production prompts, or access API keys directly.

Inputs

• Feature requirements (what the LLM should do)
• Input data format and examples
• Desired output format and constraints
• Quality requirements (accuracy, consistency, tone)
• Cost and latency constraints (model selection guidance)

Input Sanitization

No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets.

Procedure

Progress Checklist

• [ ] Step 1: Define the task precisely
• [ ] Step 2: Structure the system prompt
• [ ] Step 3: Design chain-of-thought (if applicable)
• [ ] Step 4: Design output format
• [ ] Step 5: Select and craft few-shot examples
• [ ] Step 6: Design versioning strategy

Step 1: Define the Task Precisely

Before writing a prompt, articulate:

• Input: What exactly does the model receive? (user message, context, data)
• Output: What exactly should it produce? (classification, generation, extraction, transformation)
• Constraints: What must it never do? (hallucinate facts, reveal system prompt, produce PII)
• Edge cases: What happens with empty input, adversarial input, ambiguous input?

Step 2: Structure the System Prompt

Use a layered structure:

1. Identity and role: Who is the model in this context?
2. Task description: What it's being asked to do (one paragraph, precise)
3. Constraints and rules: Hard rules it must follow (numbered list)
4. Output format: Exact structure of the expected output (with template)
5. Few-shot examples: 2-3 input/output pairs showing ideal behavior
6. Edge case handling: What to do when uncertain, off-topic, or missing data

Step 3: Design Chain-of-Thought (if applicable)

For complex reasoning tasks:

• Explicit CoT instruction: "Think step by step before answering"
• Structured CoT: Define the reasoning steps (e.g., "1. Identify the entities, 2. Determine relationships, 3. Synthesize answer")
• Hidden CoT: Instruct model to reason in a <thinking> block, then provide the answer separately
• When to skip CoT: Classification tasks, simple extraction, and format conversion rarely benefit

Step 4: Design Output Format

Specify the exact output structure:

• JSON output: Provide a schema with required fields, types, and constraints
• Markdown output: Provide a template with section headers
• Structured extraction: Define the fields and valid values explicitly
• Validation: How will the output be parsed? Design the format for reliable parsing.

Step 5: Select and Craft Few-Shot Examples

Choose examples strategically:

• Cover the range: Include examples representing different input types
• Include edge cases: Show desired behavior for tricky inputs
• Show the format: Examples should match the exact output format
• Keep it minimal: 2-3 examples are usually enough; more can confuse
• Order matters: Put the most representative example last (recency bias)

Step 6: Design Versioning Strategy

Plan for prompt evolution:

• Version identifier: Semantic versioning (v1.0, v1.1, v2.0)
• Storage: Version-controlled alongside code (not in a database, not hardcoded)
• A/B testing: How to run two prompt versions simultaneously
• Rollback: How to revert to a previous version quickly
• Changelog: What changed and why, linked to eval results

Compaction resilience: If context was lost during a long session, re-read the Inputs section to reconstruct what system is being analyzed, check the Progress Checklist for completed steps, then resume from the earliest incomplete step.

Output Format

# Prompt Design: [Feature Name]

## Task Definition
**Input:** [Description + example]
**Output:** [Description + format]
**Constraints:** [Hard rules]

## System Prompt (v1.0)

[Full system prompt text]

## Few-Shot Examples
### Example 1
**Input:** [Example input]
**Expected output:** [Example output]

### Example 2
**Input:** [Edge case input]
**Expected output:** [Edge case output]

## Chain-of-Thought Strategy
[Whether CoT is used, what the reasoning structure looks like]

## Output Schema
```json
{
  "field1": "string (required) — description",
  "field2": "number (optional) — description"
}

Prompt Versioning

| Version | Date | Change | Eval Score | |---------|------|--------|------------| | v1.0 | [Date] | Initial | [Score] |

Model Recommendation

Recommended: [Model] at [temperature] Rationale: [Why this model for this task] Cost estimate: [$/1K requests]

## Handoff

- Hand off to rag-architecture if prompt design reveals retrieval or context assembly requirements.
- Hand off to ai-evaluation if prompt versioning requires an evaluation framework or regression testing.

## Quality Checks

- [ ] System prompt has a clear role, task, constraints, format, and examples
- [ ] Output format is designed for reliable programmatic parsing
- [ ] Few-shot examples cover normal cases and at least one edge case
- [ ] Constraints address prompt injection, hallucination, and off-topic input
- [ ] Prompt is stored in version control with a changelog
- [ ] Model and temperature are justified for the task requirements

## Evolution Notes
<!-- Observations appended after each use -->
<!-- 2026-07-06 | claude-config-model-optimization | eval-before-activation framing came directly from this skill's process; keep -->
<!-- 2026-07-14 | llm-wiki-starter-repo-for-friend | greenfield doc-repo session: skill applied to reference repos / a single beginner reader; council converged cleanly, dry-run ingest validated the resulting contract -->