Architecture

Prompt patterns and user preferences live in ~/prompting/.

~/prompting/
├── memory.md          # HOT: user voice, model preferences, learned corrections
├── patterns/          # Reusable prompt templates by task type
└── history.md         # Past prompts with outcomes

See memory-template.md for initial setup.

Quick Reference

Core Rules

1. Ask Before Assuming

Before writing any prompt, ask:

• What model? (GPT-4, Claude, Haiku, Gemini)
• What's the failure mode you're seeing? (if iterating)
• Token budget? (cost-sensitive vs. quality-first)

Never default to verbose. Simpler often wins.

2. Preserve What Works

When improving a failing prompt:

• Change ONE thing at a time
• Note what's currently working
• Surgical fixes > rewrites

3. Model-Specific Adaptation

See models.md — key differences:

• Claude: explicit constraints, less scaffolding needed
• GPT-4: benefits from step-by-step, tolerates verbose
• Haiku/fast models: brevity critical, skip examples when possible

Prompt optimized for one model will underperform on others.

4. Voice Lock

When user provides writing samples:

• Extract specific patterns (sentence length, punctuation, vocabulary)
• Apply consistently throughout session
• Check output against samples before delivering

5. True Variation

When generating alternatives, vary:

• Structure (not just synonyms)
• Emotional angle
• Opening hook
• Call-to-action style

"Top 5 reasons" → "The hidden truth about" → "What nobody tells you about" = real variation.

6. Failure Classification

When a prompt fails, classify the failure type:

• Hallucination → add grounding, sources, constraints
• Format break → strengthen output spec, add examples
• Instruction drift → move critical constraints earlier
• Refusal → rephrase intent, remove ambiguity

Different failures need different fixes. See failures.md.

7. Compression Bias

Default to removing words, not adding. Test: "Does removing this line change the output?" If no, remove.

Token costs matter. A prompt that works with 50 tokens beats one that needs 500.

8. Test Case Generation

When asked to test a prompt:

• Generate edge cases (empty input, very long, special chars)
• Include adversarial inputs
• Test boundary conditions

Don't just test happy path.

9. Platform-Native Output

For content prompts, know platform constraints:

• Twitter: 280 chars, no markdown
• LinkedIn: longer ok, hashtags matter
• Instagram: emoji-friendly, visual hooks

Prompt should enforce format, not hope for it.

10. Memory Persistence

Store in ~/prompting/memory.md:

• User's preferred style (terse vs detailed)
• Target models they commonly use
• Past corrections ("I told you I don't want emojis")

Reference before every prompting task.