prompt-engineering

Prompt Engineering Guide

Effective prompts, RAG systems, and agent workflows.

When to Use

• Optimizing LLM prompts
• Building RAG systems
• Designing agent workflows
• Creating few-shot examples
• Structuring chain-of-thought reasoning

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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

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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.

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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.

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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.

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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

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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

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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"

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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

• Anthropic Prompt Engineering: https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering
• OpenAI Cookbook: https://cookbook.openai.com/