prompt-refiner

# Prompt Refiner Turn messy input into structured, AI-optimized prompts on the first try. ## When to Use - Voice transcription input (speech-to-text) - Casual, informal, or mixed-language requests (English + Chinese) - Vague or ambiguous requests (missing target, unclear scope) - Complex multi-step tasks that benefit from chaining - Before destructive actions (delete, restart, deploy) Skip if: request is already specific, task is simple/low-stakes, or user says "just do it." ## Core Framework: TCREI Google's prompt engineering framework — apply to every refined prompt: | Component | What to include | |-----------|----------------| | **Task** | Action verb + specific target. *"Summarize the sales report for Q1"* | | **Context** | Background, environment, constraints. *"Account: jamesxu81@gmail.com, NZ timezone"* | | **References** | Examples, templates, tone samples. *"Match this format: [example]"* | | **Evaluate** | How to judge the output. *"Flag any missing data"* | | **Iterate** | How to improve if result is off | ## The Process (5 Steps) ### 1. Analyze Identify: Intent · Target · Constraints · Gaps · Language ### 2. Assign Persona (Always) Give the AI a role that matches the task: - Code task → `"You are a senior Node.js engineer"` - Email task → `"You are a professional business writer"` - Data task → `"You are a data analyst specializing in sales metrics"` - Security task → `"You are a cybersecurity expert reviewing for vulnerabilities"` ### 3. Clarify (If Critical Gaps Exist) Ask **ONE** focused question — not multiple. - ✅ "Which file — `api/validate.js` or `api/auth.js`?" - ❌ "Which file? What language? What to check? When is the deadline?" ### 4. Construct the Structured Prompt ``` Persona: [Role + expertise relevant to the task] Task: [Action verb + specific target] Context: [System, environment, account, paths, dates] References: [Examples, templates, or few-shot samples when format matters] Requirements: [Constraints, scope, edge cases, what NOT to do] Output: [Format, destination, success criteria, level of detail] ``` **Advanced techniques** — apply when appropriate: - **Few-shot**: Add 1–2 input/output examples when format consistency matters - **Chain of Thought**: Add `"Think step by step:"` for complex reasoning - **Prompt Chaining**: Break multi-step tasks into linked sub-prompts - **Meta Prompting**: Ask AI to refine the prompt itself before executing See `references/techniques.md` for when/how to use each technique. ### 5. Confirm & Execute - Destructive/complex actions: Show 1-sentence summary → get confirmation - Safe/obvious tasks: Execute directly ## Quick Checklist Before executing, verify: - ✅ Persona assigned - ✅ Intent is clear (specific action + target) - ✅ Context is concrete (real paths, accounts, dates) - ✅ Requirements are testable - ✅ Output format defined - ✅ Success criteria stated ## Real Examples See `references/examples.md` for complete worked examples including: - Voice transcription (Chinese) → Gmail check - Vague code review → structured debug prompt - Mixed-language service restart - Complex multi-step task with chaining ## Common Anti-Patterns to Avoid | Anti-Pattern | Fix | |---|---| | Too many requirements in one prompt | Split into chained sub-prompts | | Vague success criteria ("write a good report") | Define measurable criteria | | No edge case handling | Add: "If X is missing, do Y" | | Tweaking temperature instead of the prompt | Improve prompt structure first | | Negative instructions only ("don't do X") | Tell it what TO do instead |