The LLM Prompt Optimizer skill systematically analyzes and refines prompts to maximize the quality, accuracy, and relevance of large language model outputs. It applies evidence-based optimization techniques including structural improvements, context enrichment, constraint calibration, and output format specification.
This skill goes beyond basic prompt writing by leveraging understanding of how different LLMs process instructions, their attention patterns, and their response tendencies. It helps you transform underperforming prompts into high-yield instructions that consistently produce the results you need.
Whether you are building production AI systems, conducting research, or simply want better ChatGPT responses, this skill ensures your prompts are optimized for your specific model and use case.
Core Workflows
Workflow 1: Analyze and Diagnose Prompt Issues
Receive
the current prompt and sample outputs
Identify
failure patterns:
Hallucination triggers
Ambiguity sources
Missing context gaps
Conflicting instructions
Over/under-constrained parameters
Map
issues to specific prompt segments
Prioritize
fixes by impact
Explain
root causes to user
Workflow 2: Apply Optimization Techniques
Select
appropriate techniques based on diagnosis:
Chain-of-thought insertion
Few-shot example addition
Role/persona specification
Output schema definition
Constraint tightening/loosening
Restructure
prompt for clarity
Add
missing context or examples
Remove
conflicting or redundant instructions
Test
optimized version
Iterate
based on results
Workflow 3: Model-Specific Optimization
Identify
target LLM (GPT-4, Claude, Llama, etc.)
Apply
model-specific best practices:
Token budget optimization
System prompt vs user prompt split
Temperature/sampling guidance
Context window utilization
Adjust
for model quirks and strengths
Document
model-specific recommendations
Quick Reference
Action
Command/Trigger
Diagnose prompt issues
"Why isn't this prompt working: [prompt]"
Optimize for accuracy
"Optimize for accuracy: [prompt]"
Reduce hallucinations
"Reduce hallucinations in: [prompt]"
Add structure
"Add better structure to: [prompt]"
Model-specific optimization
"Optimize this for [model]: [prompt]"
A/B test variants
"Create prompt variants for testing: [prompt]"
Best Practices
Start with Clear Intent
Define exactly what success looks like before optimizing
Bad: "Make it work better"
Good: "Reduce factual errors while maintaining conversational tone"
Use Explicit Output Formats
LLMs follow structure better than vague requests
Specify JSON schemas, markdown formats, or template structures
Example: "Return as JSON with keys: analysis, recommendations, confidence"
Calibrate Constraints
Too many constraints cause conflicts; too few cause drift
Test constraint combinations systematically
Remove constraints that don't improve output quality
Leverage Positive Instructions
Tell the model what TO do, not just what NOT to do
Bad: "Don't be verbose"
Good: "Respond in 2-3 concise sentences"
Position Critical Instructions Strategically
Beginning and end get more attention
Put key constraints at the start
Repeat critical requirements at the end
Use Delimiters for Multi-Part Inputs
Clear separation prevents confusion
Triple quotes, XML tags, or markdown headers
Example:
"""User Query: {query}""" """Context: {context}"""
Advanced Techniques
Recursive Refinement Loop
For complex prompts, use iterative optimization:
1. Generate baseline outputs (n=5)
2. Score outputs against criteria
3. Identify lowest-scoring dimension
4. Adjust prompt targeting that dimension
5. Repeat until all dimensions score acceptably
Prompt Decomposition
Break complex tasks into simpler sub-prompts:
Complex: "Analyze this code, find bugs, suggest fixes, and refactor"
Decomposed:
Step 1: "List all potential bugs in this code"
Step 2: "For each bug, explain the fix"
Step 3: "Refactor the fixed code for clarity"
Negative Example Injection
Show what NOT to do alongside positive examples:
Good output: [example]
Bad output (avoid this): [anti-example]
Key difference: [explanation]
Token Budget Optimization
When context is limited:
1. Remove redundant phrases
2. Use abbreviations consistently
3. Compress examples to minimal effective size
4. Prioritize recent/relevant context
5. Consider summarizing long contexts
Common Pitfalls to Avoid
Over-engineering simple prompts with unnecessary complexity
Copying prompts between models without adaptation
Ignoring the relationship between temperature and prompt specificity
Adding examples that introduce unwanted patterns
Using vague terms like "good," "proper," or "appropriate" without definition
Conflicting instructions that force the model to choose
Forgetting to specify handling of edge cases and errors