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
| Topic | File |
|---|---|
| Common failure modes | failures.md |
| Model-specific quirks | models.md |
| Iteration workflow | iteration.md |
| Advanced techniques | techniques.md |
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.