Prompt Engineering Guide
Effective prompts, RAG systems, and agent workflows.
When to Use
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Optimizing LLM prompts
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Building RAG systems
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Designing agent workflows
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Creating few-shot examples
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Structuring chain-of-thought reasoning
Prompt Structure
Core Components
Component Purpose Include When
Role/Context Set expertise, persona Complex domain tasks
Task Clear instruction Always
Format Output structure Need structured output
Examples Few-shot learning Pattern demonstration needed
Constraints Boundaries, rules Need to limit scope
Prompt Patterns
Pattern Use Case Key Concept
Chain of Thought Complex reasoning "Think step by step"
Few-Shot Pattern learning 2-5 input/output examples
Role Playing Domain expertise "You are an expert X"
Structured Output Parsing needed Specify JSON/format exactly
Self-Consistency Improve accuracy Generate multiple, vote
Chain of Thought Variants
Variant Description When to Use
Standard CoT "Think step by step" Math, logic problems
Zero-Shot CoT Just add "step by step" Quick reasoning boost
Structured CoT Numbered steps Complex multi-step
Self-Ask Ask sub-questions Research-style tasks
Tree of Thought Explore multiple paths Creative/open problems
Key concept: CoT works because it forces the model to show intermediate reasoning, reducing errors in the final answer.
Few-Shot Learning
Example Selection
Criteria Why
Representative Cover common cases
Diverse Show range of inputs
Edge cases Handle boundaries
Consistent format Teach output pattern
Number of Examples
Count Trade-off
0 (zero-shot) Less context, more creative
2-3 Good balance for most tasks
5+ Complex patterns, use tokens
Key concept: Examples teach format more than content. The model learns "how" to respond, not "what" facts to include.
RAG System Design
Architecture Flow
Query → Embed → Search → Retrieve → Augment Prompt → Generate
Chunking Strategies
Strategy Best For Trade-off
Fixed size General documents May split sentences
Sentence-based Precise retrieval Many small chunks
Paragraph-based Context preservation May be too large
Semantic Mixed content More complex
Retrieval Quality Factors
Factor Impact
Chunk size Too small = no context, too large = noise
Overlap Prevents splitting important content
Metadata filtering Narrows search space
Re-ranking Improves relevance of top-k
Hybrid search Combines keyword + semantic
Key concept: RAG quality depends more on retrieval quality than generation quality. Fix retrieval first.
Agent Patterns
ReAct Pattern
Step Description
Thought Reason about what to do
Action Call a tool
Observation Process tool result
Repeat Until task complete
Tool Design Principles
Principle Why
Single purpose Clear when to use
Good descriptions Model selects correctly
Structured inputs Reliable parsing
Informative outputs Model understands result
Error messages Guide retry attempts
Prompt Optimization
Token Efficiency
Technique Savings
Remove redundant instructions 10-30%
Use abbreviations in examples 10-20%
Compress context with summaries 50%+
Remove verbose explanations 20-40%
Quality Improvement
Technique Effect
Add specific examples Reduces errors
Specify output format Enables parsing
Include edge cases Handles boundaries
Add confidence scoring Calibrates uncertainty
Common Task Patterns
Task Key Prompt Elements
Extraction List fields, specify format (JSON), handle missing
Classification List categories, one-shot per category, single answer
Summarization Specify length, focus areas, format (bullets/prose)
Generation Style guide, length, constraints, examples
Q&A Context placement, "based only on context"
Best Practices
Practice Why
Be specific and explicit Reduces ambiguity
Provide clear examples Shows expected format
Specify output format Enables parsing
Test with diverse inputs Find edge cases
Iterate based on failures Targeted improvement
Separate instructions from data Prevent injection
Resources
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Anthropic Prompt Engineering: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering
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OpenAI Cookbook: https://cookbook.openai.com/