prompt-engineering

name	prompt-engineering
description	Interactive prompt optimization workflow for LLMs. Use when optimizing, improving, or engineering prompts for Claude, GPT, Gemini, or other language models; covers analysis, model-specific techniques, few-shot examples, XML structuring, and validation.

Interactive Prompt Optimization Workflow

Execute this workflow to systematically improve any prompt for optimal LLM performance.

Step 1: Analyze Current State

Gather baseline information about your prompt optimization task:

• Current prompt: Capture the exact prompt you want to optimize
• Target model: Identify the specific model (Claude 4.5, Gemini 3.0, GPT 5.1, etc.)
• Use case: Clarify the primary purpose (coding agent, analysis, content generation, conversation)
• Failure cases: Document specific examples where current prompt fails or underperforms
• Success criteria: Define measurable outcomes (accuracy, format compliance, response time)
• Test cases: Create 3-5 representative examples for validation

Step 2: Identify Model Type

Determine the correct prompting approach based on model architecture:

Reasoning Models (Claude 4.x, Gemini 3.0, GPT o-series, DeepSeek-R1):

• AVOID explicit CoT phrases like "think step-by-step" or "let's work through this"
• PROVIDE rich context with all relevant information upfront
• LET the model's internal reasoning handle the thinking process

Non-Reasoning Models (GPT-4o, GPT-4.1, Claude with thinking off):

• USE explicit CoT prompting with structured thinking tags
• GUIDE the reasoning process with step-by-step instructions
• STRUCTURE with <thinking> and <answer> tags

Step 3: Select Core Techniques

Based on your use case, select and apply appropriate techniques:

Essential for ALL prompts:

• Few-shot examples: Add 3-5 diverse, representative examples
• XML structure: Use tags to separate role, context, examples, task
• Output format: Explicitly specify desired response format
• Context: Provide all necessary information and constraints

For complex tasks:

• Prompt chaining: Break into sequential subtasks with clear handoffs
• Role assignment: Define expert persona in system prompt
• Long context handling: Place lengthy data first, extract relevant quotes

For coding agents:

• Parallel tool calling: Instruct to call independent tools simultaneously
• Hallucination prevention: Require reading files before answering
• Action bias: Encourage implementation over suggestion
• Solution persistence: Emphasize completing tasks end-to-end

For analysis tasks:

• Structured thinking: Require step-by-step reasoning
• Quote grounding: Extract relevant quotes before analysis
• Cross-referencing: Instruct to verify across multiple sources

Step 4: Apply Techniques Systematically

Transform your prompt using selected techniques:

1. Add XML structure:

<role>You are a [domain] expert specializing in [area].</role>

<context>[Relevant background information, constraints, requirements]</context>

<examples
>[3-5 diverse examples showing desired input/output patterns]</examples>

<task>[Specific user request with explicit constraints and output format]</task>

2. Insert representative examples:

• Show desired behavior through concrete examples
• Use consistent formatting across all examples
• Include edge cases if relevant
• Demonstrate proper structure and tone

3. Apply model-specific optimization:

• Reasoning models: Provide comprehensive context without prescriptive thinking instructions
• Non-reasoning models: Add explicit Chain of Thought prompts and thinking tags

4. Specify constraints and format:

• Define output structure explicitly
• List what to include and what to avoid
• Provide length guidelines if applicable
• Specify handling of edge cases

Step 5: Test and Validate

Run empirical tests to verify improvements:

1. Test on failure cases:

• Run optimized prompt on all documented failure cases
• Verify each now produces acceptable output

2. Test edge cases:

• Boundary conditions (empty input, maximum length, etc.)
• Unusual but valid inputs
• Ambiguous scenarios

3. Compare metrics:

• Accuracy improvement (before vs after)
• Format compliance rate
• Response consistency across runs
• Execution time (if relevant)

4. Iterate on gaps:

• If issues persist: Rephrase instructions for clarity
• If format violations: Make format requirements more explicit
• If inconsistent: Add more examples showing desired behavior
• If slow: Simplify prompt or adjust model parameters

Handle fallback responses:

• If model refuses or gives generic responses:
  • Increase temperature parameter
  • Rephrase request to avoid trigger words
  • Check for safety filter activation
  • Try different framing of the same task

Step 6: Deliver Optimized Prompt

Package your work for deployment or handoff:

1. Final prompt template:

• Complete, production-ready prompt text
• Clearly marked sections (role, context, examples, task)
• Proper formatting and structure

2. Technique annotations:

• Document which techniques were applied and why
• Note model-specific considerations
• Highlight critical sections

3. Model-specific callouts:

• Parameter recommendations (temperature, max_tokens, etc.)
• Model family compatibility notes
• Performance characteristics

4. Before/after metrics:

• Quantitative improvement measurements
• Specific failure cases resolved
• Remaining limitations or edge cases

5. Usage guidelines:

• When to use this prompt
• How to adapt for variations
• Common pitfalls to avoid

6. Known edge cases:

• Scenarios where prompt may still struggle
• Recommended fallback approaches
• Future improvement opportunities