Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
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)?
