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.
- 📺 YouTube Channel: Income Stream Surfers - Learn advanced AI automation techniques
- 🏫 AI Automation School: Skool Community - Join our AI automation community
- 🌐 SEO Grove: Live Website - See advanced AI automation in action (built with different methods)
For full setup with automatic documentation hooks: See SETUP.md
For basic template usage:
# 1. Clone this template
git clone https://github.com/IncomeStreamSurfer/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- What is Context Engineering?
- Template Structure
- Step-by-Step Guide
- Writing Effective INITIAL.md Files
- The PRP Workflow
- Using Examples Effectively
- Best Practices
Context Engineering represents a paradigm shift from traditional prompt engineering:
Prompt Engineering:
- Focuses on clever wording and specific phrasing
- Limited to how you phrase a task
- Like giving someone a sticky note
Context Engineering:
- A complete system for providing comprehensive context
- Includes documentation, examples, rules, patterns, and validation
- Like writing a full screenplay with all the details
- Reduces AI Failures: Most agent failures aren't model failures - they're context failures
- Ensures Consistency: AI follows your project patterns and conventions
- Enables Complex Features: AI can handle multi-step implementations with proper context
- Self-Correcting: Validation loops allow AI to fix its own mistakes
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. ;)
The CLAUDE.md file contains project-wide rules that the AI assistant will follow in every conversation. The template includes:
- Project awareness: Reading planning docs, checking tasks
- Code structure: File size limits, module organization
- Testing requirements: Unit test patterns, coverage expectations
- Style conventions: Language preferences, formatting rules
- Documentation standards: Docstring formats, commenting practices
You can use the provided template as-is or customize it for your project.
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.
PRPs (Product Requirements Prompts) are comprehensive implementation blueprints that include:
- Complete context and documentation
- Implementation steps with validation
- Error handling patterns
- Test requirements
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.mdNote: The slash commands are custom commands defined in .claude/commands/. You can view their implementation:
.claude/commands/generate-prp.md- See how it researches and creates PRPs.claude/commands/execute-prp.md- See how it implements features from PRPs
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:
- Read your feature request
- Research the codebase for patterns
- Search for relevant documentation
- Create a comprehensive PRP in
PRPs/your-feature-name.md
Once generated, execute the PRP to implement your feature:
/execute-prp PRPs/your-feature-name.mdThe AI coding assistant will:
- Read all context from the PRP
- Create a detailed implementation plan
- Execute each step with validation
- Run tests and fix any issues
- Ensure all success criteria are met
FEATURE: Be specific and comprehensive
- ❌ "Build a web scraper"
- ✅ "Build an async web scraper using BeautifulSoup that extracts product data from e-commerce sites, handles rate limiting, and stores results in PostgreSQL"
EXAMPLES: Leverage the examples/ folder
- Place relevant code patterns in
examples/ - Reference specific files and patterns to follow
- Explain what aspects should be mimicked
DOCUMENTATION: Include all relevant resources
- API documentation URLs
- Library guides
- MCP server documentation
- Database schemas
OTHER CONSIDERATIONS: Capture important details
- Authentication requirements
- Rate limits or quotas
- Common pitfalls
- Performance requirements
The command follows this process:
-
Research Phase
- Analyzes your codebase for patterns
- Searches for similar implementations
- Identifies conventions to follow
-
Documentation Gathering
- Fetches relevant API docs
- Includes library documentation
- Adds gotchas and quirks
-
Blueprint Creation
- Creates step-by-step implementation plan
- Includes validation gates
- Adds test requirements
-
Quality Check
- Scores confidence level (1-10)
- Ensures all context is included
- Load Context: Reads the entire PRP
- Plan: Creates detailed task list using TodoWrite
- Execute: Implements each component
- Validate: Runs tests and linting
- Iterate: Fixes any issues found
- Complete: Ensures all requirements met
See PRPs/EXAMPLE_multi_agent_prp.md for a complete example of what gets generated.
The examples/ folder is critical for success. AI coding assistants perform much better when they can see patterns to follow.
-
Code Structure Patterns
- How you organize modules
- Import conventions
- Class/function patterns
-
Testing Patterns
- Test file structure
- Mocking approaches
- Assertion styles
-
Integration Patterns
- API client implementations
- Database connections
- Authentication flows
-
CLI Patterns
- Argument parsing
- Output formatting
- Error handling
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
- Don't assume the AI knows your preferences
- Include specific requirements and constraints
- Reference examples liberally
- More examples = better implementations
- Show both what to do AND what not to do
- Include error handling patterns
- PRPs include test commands that must pass
- AI will iterate until all validations succeed
- This ensures working code on first try
- Include official API docs
- Add MCP server resources
- Reference specific documentation sections
- Add your conventions
- Include project-specific rules
- Define coding standards
This template demonstrates an advanced PRP creation method using multiple parallel research agents for comprehensive documentation gathering.
- SEO Grove: https://seogrove.ai/ - Example of advanced AI automation (built with different methods)
- YouTube Channel: https://www.youtube.com/c/incomestreamsurfers - Learn more about AI automation methodologies
- AI Automation School: https://www.skool.com/iss-ai-automation-school-6342/about - Join our community
read my incredibly specific instructions about how to create a prp document then summarise them, also store how to do a jina scrapein order to create a llm.txt in your memory
If a page 404s or does not scrape properly, scrape it again
Do not use Jina to scrape CSS of the design site.
All SEPARATE pages must be stored in /research/[technology]/ directories with individual .md files.
curl
"https://r.jina.ai/https://platform.openai.com/docs/" \
-H "Authorization: Bearer jina_033257e7cdf14fd3b948578e2d34986bNtfCCkjHt7_j1Bkp5Kx521rDs2Eb"
/generate-prp initial.md
Wait until it gets to the research phase, then press escape and say:
can you spin up multiple research agents and do this all at the same time
This approach enables:
- Parallel Documentation Scraping: 6+ agents simultaneously research different technologies
- Comprehensive Coverage: 30-100+ pages of official documentation scraped and organized
- Technology-Specific Organization: Each technology gets its own
/research/[tech]/directory - Production-Ready PRPs: Complete implementation blueprints with real-world examples
research/
├── pydantic-ai/ # 22+ documentation pages
├── openai/ # 20+ API documentation pages
├── anthropic/ # 18+ Claude documentation pages
├── jina/ # 12+ scraping API pages
├── shopify/ # 18+ GraphQL/REST API pages
└── seo-apis/ # 24+ Search Console/Ahrefs pages
This multi-agent research approach results in PRPs with 9/10 confidence scores for one-pass implementation success.