llm-prompt-optimizer

LLM Prompt Optimizer

Overview

This skill transforms weak, vague, or inconsistent prompts into precision-engineered instructions that reliably produce high-quality outputs from any LLM (Claude, Gemini, GPT-4, Llama, etc.). It applies systematic prompt engineering frameworks — from zero-shot to few-shot, chain-of-thought, and structured output patterns.

When to Use This Skill

• Use when a prompt returns inconsistent, vague, or hallucinated results
• Use when you need structured/JSON output from an LLM reliably
• Use when designing system prompts for AI agents or chatbots
• Use when you want to reduce token usage without sacrificing quality
• Use when implementing chain-of-thought reasoning for complex tasks
• Use when prompts work on one model but fail on another

Step-by-Step Guide

1. Diagnose the Weak Prompt

Before optimizing, identify which problem pattern applies:

Problem	Symptom	Fix
Too vague	Generic, unhelpful answers	Add role + context + constraints
No structure	Unformatted, hard-to-parse output	Specify output format explicitly
Hallucination	Confident wrong answers	Add "say I don't know if unsure"
Inconsistent	Different answers each run	Add few-shot examples
Too long	Verbose, padded responses	Add length constraints

2. Apply the RSCIT Framework

Every optimized prompt should have:

• R — Role: Who is the AI in this interaction?
• S — Situation: What context does it need?
• C — Constraints: What are the rules and limits?
• I — Instructions: What exactly should it do?
• T — Template: What should the output look like?

Before (weak prompt):

Explain machine learning.

After (optimized prompt):

You are a senior ML engineer explaining concepts to a junior developer.

Context: The developer has 1 year of Python experience but no ML background.

Task: Explain supervised machine learning in simple terms.

Constraints:
- Use an analogy from everyday life
- Maximum 200 words
- No mathematical formulas
- End with one actionable next step

Format: Plain prose, no bullet points.

3. Chain-of-Thought (CoT) Pattern

For reasoning tasks, ask for a concise explanation, key assumptions and checkable result rather than private internal chain-of-thought:

Solve the problem and give the final answer with a concise explanation of the key steps and verification.

Problem: [your problem here]

Thinking process:
Step 1: [identify what's given]
Step 2: [identify what's needed]
Step 3: [apply logic or formula]
Step 4: [verify the answer]

Final Answer:

4. Few-Shot Examples Pattern

Provide 2-3 examples to establish the pattern:

Classify the sentiment of customer reviews as POSITIVE, NEGATIVE, or NEUTRAL.

Examples:
Review: "This product exceeded my expectations!" -> POSITIVE
Review: "It arrived broken and support was useless." -> NEGATIVE  
Review: "Product works as described, nothing special." -> NEUTRAL

Now classify:
Review: "[your review here]" ->

5. Structured JSON Output Pattern

Extract the following information from the text below and return it as valid JSON only.
Do not include any explanation or markdown — just the raw JSON object.

Schema:
{
  "name": string,
  "email": string | null,
  "company": string | null,
  "role": string | null
}

Text: [input text here]

6. Reduce Hallucination Pattern

Answer the following question based ONLY on the provided context.
If the answer is not contained in the context, respond with exactly: "I don't have enough information to answer this."
Do not make up or infer information not present in the context.

Context:
[your context here]

Question: [your question here]

7. Prompt Compression Techniques

Reduce token count without losing effectiveness:

# Verbose (expensive)
"Please carefully analyze the following code and provide a detailed explanation of 
what it does, how it works, and any potential issues you might find."

# Compressed (efficient, same quality)
"Analyze this code: explain what it does, how it works, and flag any issues."

Best Practices

• ✅ Do: Always specify the output format (JSON, markdown, plain text, bullet list)
• ✅ Do: Use delimiters (```, ---) to separate instructions from content
• ✅ Do: Test prompts with edge cases (empty input, unusual data)
• ✅ Do: Version your system prompts in source control
• ✅ Do: Add "think step by step" for math, logic, or multi-step tasks
• ❌ Don't: Use negative-only instructions ("don't be verbose") — add positive alternatives
• ❌ Don't: Assume the model knows your codebase context — always include it
• ❌ Don't: Use the same prompt across different models without testing — they behave differently

Prompt Audit Checklist

Before using a prompt in production:

• Does it have a clear role/persona?
• Is the output format explicitly defined?
• Are edge cases handled (empty input, ambiguous data)?
• Is the length appropriate (not too long/short)?
• Has it been tested on 5+ varied inputs?
• Is hallucination risk addressed for factual tasks?

Troubleshooting

Problem: Model ignores format instructions Solution: Use supported structured-output/schema constraints where available and validate responses with a parser. Prompt wording alone cannot guarantee valid JSON; bound retries and handle refusal or truncation.

Problem: Inconsistent results between runs Solution: Lower the temperature setting (0.0-0.3 for factual tasks). Add more few-shot examples.

Problem: Prompt works in playground but fails in production Solution: Check if system prompt is being sent correctly. Verify token limits aren't being exceeded (use a token counter).

Problem: Output is too long Solution: Add explicit word/sentence limits: "Respond in exactly 3 bullet points, each under 20 words."

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.