context-engineering-intro README

The project's README from github.com/coleam00/context-engineering-intro. As one markdown file: README.md.

Context Engineering Template

A comprehensive template for getting started with Context Engineering - the discipline of engineering context for AI coding assistants so they have the information necessary to get the job done end to end.

Context Engineering is 10x better than prompt engineering and 100x better than vibe coding.

🚀 Quick Start

# 1. Clone this template
git clone https://github.com/coleam00/Context-Engineering-Intro.git
cd Context-Engineering-Intro

# 2. Set up your project rules (optional - template provided)
# Edit CLAUDE.md to add your project-specific guidelines

# 3. Add examples (highly recommended)
# Place relevant code examples in the examples/ folder

# 4. Create your initial feature request
# Edit INITIAL.md with your feature requirements

# 5. Generate a comprehensive PRP (Product Requirements Prompt)
# In Claude Code, run:
/generate-prp INITIAL.md

# 6. Execute the PRP to implement your feature
# In Claude Code, run:
/execute-prp PRPs/your-feature-name.md

📚 Table of Contents

What is Context Engineering?

Context Engineering represents a paradigm shift from traditional prompt engineering:

Prompt Engineering vs Context Engineering

Prompt Engineering:

Context Engineering:

Why Context Engineering Matters

  1. Reduces AI Failures: Most agent failures aren't model failures - they're context failures
  2. Ensures Consistency: AI follows your project patterns and conventions
  3. Enables Complex Features: AI can handle multi-step implementations with proper context
  4. Self-Correcting: Validation loops allow AI to fix its own mistakes

Template Structure

context-engineering-intro/
├── .claude/
│   ├── commands/
│   │   ├── generate-prp.md    # Generates comprehensive PRPs
│   │   └── execute-prp.md     # Executes PRPs to implement features
│   └── settings.local.json    # Claude Code permissions
├── PRPs/
│   ├── templates/
│   │   └── prp_base.md       # Base template for PRPs
│   └── EXAMPLE_multi_agent_prp.md  # Example of a complete PRP
├── examples/                  # Your code examples (critical!)
├── CLAUDE.md                 # Global rules for AI assistant
├── INITIAL.md               # Template for feature requests
├── INITIAL_EXAMPLE.md       # Example feature request
└── README.md                # This file

This template doesn't focus on RAG and tools with context engineering because I have a LOT more in store for that soon. ;)

Step-by-Step Guide

1. Set Up Global Rules (CLAUDE.md)

The CLAUDE.md file contains project-wide rules that the AI assistant will follow in every conversation. The template includes:

You can use the provided template as-is or customize it for your project.

2. Create Your Initial Feature Request

Edit INITIAL.md to describe what you want to build:

## FEATURE:
[Describe what you want to build - be specific about functionality and requirements]

## EXAMPLES:
[List any example files in the examples/ folder and explain how they should be used]

## DOCUMENTATION:
[Include links to relevant documentation, APIs, or MCP server resources]

## OTHER CONSIDERATIONS:
[Mention any gotchas, specific requirements, or things AI assistants commonly miss]

See INITIAL_EXAMPLE.md for a complete example.

3. Generate the PRP

PRPs (Product Requirements Prompts) are comprehensive implementation blueprints that include:

They are similar to PRDs (Product Requirements Documents) but are crafted more specifically to instruct an AI coding assistant.

Run in Claude Code:

/generate-prp INITIAL.md

Note: The slash commands are custom commands defined in .claude/commands/. You can view their implementation:

The $ARGUMENTS variable in these commands receives whatever you pass after the command name (e.g., INITIAL.md or PRPs/your-feature.md).

This command will:

  1. Read your feature request
  2. Research the codebase for patterns
  3. Search for relevant documentation
  4. Create a comprehensive PRP in PRPs/your-feature-name.md

4. Execute the PRP

Once generated, execute the PRP to implement your feature:

/execute-prp PRPs/your-feature-name.md

The AI coding assistant will:

  1. Read all context from the PRP
  2. Create a detailed implementation plan
  3. Execute each step with validation
  4. Run tests and fix any issues
  5. Ensure all success criteria are met

Writing Effective INITIAL.md Files

Key Sections Explained

FEATURE: Be specific and comprehensive

EXAMPLES: Leverage the examples/ folder

DOCUMENTATION: Include all relevant resources

OTHER CONSIDERATIONS: Capture important details

The PRP Workflow

How /generate-prp Works

The command follows this process:

  1. Research Phase

    • Analyzes your codebase for patterns
    • Searches for similar implementations
    • Identifies conventions to follow

  2. Documentation Gathering

    • Fetches relevant API docs
    • Includes library documentation
    • Adds gotchas and quirks

  3. Blueprint Creation

    • Creates step-by-step implementation plan
    • Includes validation gates
    • Adds test requirements

  4. Quality Check

    • Scores confidence level (1-10)
    • Ensures all context is included

How /execute-prp Works

  1. Load Context: Reads the entire PRP
  2. Plan: Creates detailed task list using TodoWrite
  3. Execute: Implements each component
  4. Validate: Runs tests and linting
  5. Iterate: Fixes any issues found
  6. Complete: Ensures all requirements met

See PRPs/EXAMPLE_multi_agent_prp.md for a complete example of what gets generated.

Using Examples Effectively

The examples/ folder is critical for success. AI coding assistants perform much better when they can see patterns to follow.

What to Include in Examples

  1. Code Structure Patterns

    • How you organize modules
    • Import conventions
    • Class/function patterns

  2. Testing Patterns

    • Test file structure
    • Mocking approaches
    • Assertion styles

  3. Integration Patterns

    • API client implementations
    • Database connections
    • Authentication flows

  4. CLI Patterns

    • Argument parsing
    • Output formatting
    • Error handling

Example Structure

examples/
├── README.md           # Explains what each example demonstrates
├── cli.py             # CLI implementation pattern
├── agent/             # Agent architecture patterns
│   ├── agent.py      # Agent creation pattern
│   ├── tools.py      # Tool implementation pattern
│   └── providers.py  # Multi-provider pattern
└── tests/            # Testing patterns
    ├── test_agent.py # Unit test patterns
    └── conftest.py   # Pytest configuration

Best Practices

1. Be Explicit in INITIAL.md

2. Provide Comprehensive Examples

3. Use Validation Gates

4. Leverage Documentation

5. Customize CLAUDE.md

Resources

context-engineering-intro (coleam00/context-engineering-intro)

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13.9K ·GitHub
Context-engineering-intro
What is this?A comprehensive template for practicing context engineering: giving AI coding assistants like Claude Code the documentation, examples and rules they need to finish features end to end.
Is this an app I install, or a set of files I copy into my project?It is a template repo you copy, not an app: a CLAUDE.md rules file, an INITIAL.md feature-request file, an examples/ folder and two custom Claude Code slash commands that you adapt to your own project.
How does the workflow actually go once I have it?Describe the feature in INITIAL.md, run /generate-prp to research the codebase and write a blueprint into PRPs/, then run /execute-prp so the assistant plans, implements and iterates until tests pass.
Do I need Claude Code specifically, or will another AI assistant work?It is built around Claude Code - the /generate-prp and /execute-prp commands live in .claude/commands/ - but the repo description notes the same strategy can be applied with any AI coding assistant.
Is there anything it deliberately leaves out?RAG and tools are out of scope for now - the author says he has a lot more in store for that - so the template currently covers global rules, feature requests and the PRP workflow only.
How is context engineering different from prompt engineering?Prompt engineering is clever wording of a single task - like a sticky note. Context engineering is a whole system of documentation, examples, rules, patterns and validation - like a full screenplay. The author calls it 10x better than prompt engineering and 100x better than vibe coding.
What exactly is a PRP?A Product Requirements Prompt: an implementation blueprint with complete context and documentation, step-by-step implementation with validation gates, error handling patterns and test requirements - like a PRD but written specifically to instruct an AI coding assistant.
Why does the examples/ folder matter so much?Because AI assistants perform much better when they can see patterns to follow. You place code structure, testing, integration and CLI patterns from your project there - showing both what to do and what not to do - so generated code matches your conventions.
What are similar projects?vibe-coding-cn, claude-code-best-practice, claude-code-ultimate-guide, vibe-wise, Coding-Agent-Skills, vibe-coding-cn, superpowers-zh, advanced-context-engineering-for-coding-agents
Which topics is it in?agentic-coding, vibe-coding, context-engineering, template, best-practices, workflow, prompt-engineering, documentation, coding-agent, claude, learning, markdown
Creator
coleam00
Size
1.5 MB
Stars
13.9K
Updated
CommitMar 16
Age
1y
Commits · 90 Days