Prompt Refiner

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name: prompt-refiner description: Transforms casual or voice-transcribed user requests into precise, AI-optimized prompts. Handles mixed languages, vague input, and ambiguity. Reduces task execution time by 2-3x and improves accuracy by 40-60%. Applies prompt engineering best practices including persona assignment, few-shot examples, chain of thought, and prompt chaining.

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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 |