Implementation Checklist

Implementation Checklist

To close the report, here's a practical checklist for implementing context engineering in a vibe coding project:

1. Define Key Context Artifacts: Identify what knowledge is crucial (e.g., architecture overview, API specs, decisions log, coding guidelines). Create initial versions of these artifacts (documents or files).

2. Set Up Context Storage: Choose where these artifacts live for AI access. Options: within repo (markdown files), external wiki (Notion/Confluence) with API access, or specialized DB/index (Pinecone, etc.). Ensure it's accessible to your AI tools (e.g., via an extension or MCP server).

3. Prime AI with Project Overview: At project or session start, feed the AI a concise project summary and any critical rules (e.g., "We use framework X, avoid Y"). Use system message or initial prompt. This reduces missteps.

4. Use a Prompt Template: When asking the AI to do a task, structure your prompt to include context sections: e.g., "Background (what has been done, relevant files/functions), Goal, Constraints (e.g., performance needs), Where to look (if specific files or docs are relevant)." This ensures no key info is left out.

5. Leverage IDE Features: Enable and use IDE search integration (like VS Code's #codebase search or Cursor's file tagging). Example: tag important files in Cursor with @, or use Copilot's "#include file" to add it to chat context . . 68

6. Persistent Memory Aids: Maintain a "session memory" note (or use Cursor Notepad / Windsurf rule) to jot important points concluded during coding. Refer the AI to it regularly ("Refer to Notepad: DB schema").

7. After an Error, Feed it Back: If AI code fails or is incorrect, supply the error message or correct reasoning back into the conversation. E.g., "The test output says X, that means our approach was flawed. Let's adjust." This prevents repetition of the same mistake.

8. Frequent Context Sync: After finishing a feature or significant change, update the context artifacts. E.g., add new endpoints to API list, update architecture diagram if needed, note any decisions made. Even 2-3 bullet points can suffice. This prevents context from drifting out of date.

9. Automate Where Possible: Set up a script or action to regenerate embeddings or documentation from code comments (if using those) on each commit or daily. Run tests and maybe an AI summary of test results as part of CI to catch any logic or context drift issues.

10. Review AI Suggestions for Context Awareness: When the AI provides an answer or code, verify it considered relevant context. If it missed something obvious (like a similar function exists already), that's a signal to improve context next time (maybe you forgot to inform it). Over time, this feedback loop will sharpen your context supply.

11. Team Alignment: If on a team, ensure everyone uses the same context resources and knows how to update them. Possibly hold a brief "context sync meeting" to review if AI is causing any friction and adjust processes.

12. Monitor & Adjust: Keep an eye on how often the AI asks for info or makes mistakes due to missing context. Use that to refine what you feed it. For example, if it frequently forgets the DB schema, incorporate that schema in the rules or give it a one-glance summary to hold. .

By following this checklist, vibe coders can gradually build a robust context engineering practice. The result will be an AI partner that feels much more "in tune" with the project – truly earning the label of a collaborator rather than a naive assistant. This not only boosts productivity but also confidence that AI-generated code aligns with the project's intent and integrity.