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, instruct the model to think step-by-step:

Solve this problem step by step, showing your work at each stage.
Only provide the final answer after completing all reasoning steps.

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: Move format instructions to the END of the prompt, after examples. Use strong language: "You MUST return only valid JSON."

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."

llm-prompt-optimizer