Agentic Prompting Tips

Once you have generated your context.md file, the next step is providing it to your AI Agent (like Claude 3.5 Sonnet, GPT-4o, or Gemini 1.5 Pro). To get the highest quality output, you should treat this file as the agent's Primary Source of Truth.

Here is how to effectively prompt an agent using the high-density context produced by ContextMD.

1. The "Grounding" Prompt

When you upload or paste the contents of context.md, start with a system instruction that anchors the agent to the document. This prevents the LLM from relying on outdated training data.

Recommended Prompt:

"I am providing a consolidated documentation file for [Project Name]. Use this file as your primary source of truth for all technical queries. If the documentation contradicts your internal knowledge, prioritize the provided file. If a specific API or feature is not mentioned in this file, state that it is not in the documentation."

2. Role-Based Contextualization

ContextMD strips away the "fluff," leaving pure logic and signatures. To re-introduce the necessary "thinking" framework, assign a specific role to your agent based on your task.

For Feature Implementation: "You are a Senior Engineer. Using the attached documentation, implement a [feature name] that follows the patterns and best practices shown in the code examples."
For Debugging: "You are a Lead QA. Based on the API signatures in this context file, identify why the following code might be failing: [your code snippet]."

3. Leveraging "Dense" Knowledge

Because ContextMD uses AI to refine the text into high-density markdown, the agent doesn't need to "read" through introductory paragraphs. You can ask very specific, cross-page questions:

Pattern Matching: "Scan the documentation and list all components that require a Provider wrapper."
Dependency Mapping: "Based on the 'Source' headers in this file, which modules are required to initialize the Auth flow?"

4. Handling Token Limits

For very large documentation sites (over 100 pages), even a refined context.md can be large.

Claude (200k+ context): Usually handles the entire file effortlessly.
GPT-4o (128k context): Handles most documentation, but for massive projects, consider generating context for specific sub-directories using the --limit flag.
Tip: If the file is extremely large, ask the agent to: "Summarize the core architectural patterns found in this context file before we start coding." This forces the agent to index the content into its active memory.

5. Multi-File Workflow

If you are using a tool like Claude Dev, Cursor, or Aider, you can keep context.md in your root directory.

In Cursor/Windsurf: Add @context.md to your chat and say: "Check the docs in context.md to see if I'm using the latest version of the submit() function."

6. Example Prompt Template

Copy and paste this alongside your context.md file for immediate results:

I have attached a context file generated from the [Library Name] documentation.
Task: [e.g., Build a custom hook for data fetching]

Constraints:
1. Use only the syntax and API versions defined in the context file.
2. Follow the architectural patterns (functional/OO) found in the examples.
3. If the documentation provides a "Best Practice" or "Warning" section for this feature, ensure the solution adheres to it.

Context File attached: [context.md]