Transforms user prompts into optimized prompts using established frameworks (RTF, RISEN, Chain of Thought, RODES). Analyzes intent, selects framework, and produces structured, ready-to-use prompts.
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Transform vague or unstructured prompts into highly effective, framework-backed prompts that maximize AI output quality.
Work silently — no framework jargon in the output. The user gets a polished prompt, not a lecture on prompt engineering theory.
Read the raw prompt and detect:
| Dimension | Options |
|---|---|
| Type | Coding, writing, analysis, design, learning, planning, creative |
| Complexity | Simple (one-step), moderate (multi-step), complex (reasoning needed) |
| Clarity | Clear intent vs. ambiguous/vague |
| Domain | Technical, business, creative, academic |
Identify implicit requirements:
| Task Type | Framework | Why |
|---|---|---|
| Role-based tasks (act as expert) | RTF (Role-Task-Format) | Clear role + task + output format |
| Step-by-step reasoning (debugging, analysis) | Chain of Thought | Forces explicit reasoning steps |
| Multi-phase projects (systems, deliverables) | RISEN (Role, Instructions, Steps, End goal, Narrowing) | Comprehensive structure |
| Complex design/architecture | RODES (Role, Objective, Details, Examples, Sense check) | Balances detail with validation |
| Summarization/synthesis | Chain of Density | Iterative compression to essentials |
| Communication (reports, presentations) | RACE (Role, Audience, Context, Expectation) | Audience-aware messaging |
| Goal-setting (OKRs, planning) | CLEAR (Collaborative, Limited, Emotional, Appreciable, Refinable) | Actionable goals |
Blending: Combine 2 frameworks when a task spans types. Example: architecture design → RODES + Chain of Thought.
Selection happens silently — never explain framework choice to the user.
Build the optimized prompt using the selected framework(s):
Structure rules:
Quality checks before presenting:
Present the optimized prompt in a clean markdown code block. No meta-commentary, no framework explanation.
User: "help me write better code"
Output:
Role: You are a senior software engineer conducting a code review.
Task: Review the code I provide and identify issues across these dimensions:
1. Readability — naming, structure, comments
2. Correctness — logic errors, edge cases, off-by-one errors
3. Performance — unnecessary allocations, O(n²) where O(n) exists
4. Security — injection risks, data exposure, input validation
5. Maintainability — coupling, cohesion, testability
Format: For each issue found, provide:
- Location (file/line if applicable)
- Category (readability/correctness/performance/security/maintainability)
- Current code
- Suggested improvement with explanation
- Severity (critical/warning/suggestion)
User: "I need to build a recommendation system using purchase history and user preferences"
Output:
Role: You are a senior ML engineer specializing in recommendation systems.
Objective: Design a product recommendation system combining user purchase history, stated preferences, and behavioral signals.
Details:
- Input sources: transaction logs, preference surveys, browsing behavior
- Algorithm tradeoffs: collaborative filtering vs. content-based vs. hybrid
- Performance targets: recommendations served in <200ms, updated hourly
- Scale: handle 100K+ users with sparse interaction matrices
Approach this step-by-step:
1. Analyze data sources and their signal quality
2. Propose 2-3 algorithm approaches with tradeoff analysis
3. Design the data pipeline (ingestion → processing → serving)
4. Define evaluation metrics (precision, recall, diversity, novelty)
5. Outline a phased implementation roadmap
Examples: Include pseudocode for the core recommendation logic, a data flow diagram, and sample API response structure.
Sense check: Verify the solution balances personalization with discovery, handles cold-start problems for new users, and scales to production load.
Output: Technical design document with architecture diagrams, algorithm justification, and implementation roadmap.
User: "my code isn't working, needs debugging"
Output:
You are an expert debugger.
Task: Debug the code I provide using systematic analysis.
Step-by-step approach:
1. Read the code and identify its intended purpose
2. Identify the error or unexpected behavior
3. Trace variable states through execution
4. Locate the root cause
5. Propose a fix with explanation
6. Suggest preventive measures
For each step, show your reasoning:
- What you're checking
- What you found
- Why it matters
Output format:
- **Issue**: [Describe the bug]
- **Root cause**: [Why it's happening]
- **Fix**: [Corrected code with inline comments]
- **Prevention**: [How to avoid this in the future]
Include a minimal working example to verify the fix.
NEVER:
ALWAYS:
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