Maintaining Context Files

Context files are living artifacts.

They don’t just describe your system, they mirror it, and as your code, architecture, or processes change, your context must evolve too.

A well-maintained context file is accurate, relevant, and aligned with the current state of your world. A stale one can quietly undermine every AI interaction built on top of it.

The Goal of Maintenance

The purpose of maintaining a context file isn’t to keep it perfect — it’s to keep it in sync.
Context represents your system’s truth at a given moment, so whenever that truth changes, your file should adapt with it.

Think of your context file like documentation for an AI coworker: if it’s out of date, that “coworker” will keep making the same bad assumptions over and over again.

The goal is simple: keep the AI’s understanding aligned with reality.

General Tips for Keeping Files Up to Date

Like any piece of technical documentation, a few simple habits go a long way in keeping context fresh and reliable:

• Review regularly.
  Treat context like living docs — review it at predictable intervals (for example, once per sprint or release). Add a “Last updated” note so it’s always clear when it was last reviewed.

• Version it with your code.
  Keep context files in your repo alongside the systems they describe. This keeps them version-controlled, reviewable, and diffable like any other asset.

• Assign ownership.
  Someone — ideally the team that owns the feature or service — should be responsible for maintaining the context. A file without ownership quickly drifts.

• Keep them modular internally, not externally.
  Use clear internal sectioning (## Architecture, ## Conventions, ## Known Issues) so updates are easier to make. But avoid scattering a single source of context across multiple files — having one source of truth helps the AI work efficiently and token-consciously.

• Reference your sources.
  If a section summarizes another document or code area, include a quick pointer (for example, “see /src/middleware/auth.js”). This helps maintainers know where updates should come from.

Maintaining AI-Generated Context Files

Using an AI to automate the context engineering process is a great way to create a strong starting point — especially for large or evolving systems.

Having an AI generate something like a copilot-instructions.md file can quickly give you a structured foundation to work from. But once you start refining it manually — adding explanations, edge cases, or nuanced guidance — keeping that balance between automation and human input becomes key.

Here’s how to do it well.

Merge, Don’t Replace

When you regenerate a context file with AI, don’t overwrite everything.

Instead, follow this simple merge process to evolve the file intelligently and keep manual insights intact.

Step-by-Step Formula for Merging with AI

1. Generate a fresh draft
   Collaborate with your AI agent to create an updated version of the file (for example, copilot-instructions.new.md).
   This gives you a snapshot of what the AI thinks your system looks like now.

2. Ask the AI to compare versions
   Prompt the AI directly:

   “Compare copilot-instructions.md with copilot-instructions.new.md and identify what’s changed, outdated, or missing.”
   Have it summarize the differences section by section. This gives you a quick overview without manually scanning every line.

3. Review each difference with purpose
   For every change the AI highlights, ask three questions:

   • Is the new info more accurate (does it reflect current reality)?

   • Is it more concise or clearer than the old version?

   • Does it add real value to how the AI will perform?
     Keep only what passes at least one of those tests.

4. Ask the AI to apply selective updates
   Once you’ve identified what’s useful, have the AI merge those changes into the existing file:

   “Update the Architecture and Conventions sections with these improvements, but keep all manual notes unchanged.”
   This produces a merged, improved file without losing human-written sections.

5. Manually verify critical parts
   Double-check anything that references code paths, dependencies, or file names — the AI can miss subtle differences.
   Confirm those details before finalizing.

6. Clean up and commit
   Commit the new version so it’s tracked with your codebase, be sure to use a descriptive commit message.

Preserve Manual Additions with Clear Boundaries

If you regularly make manual edits or add commentary, create a dedicated section in the same file for those additions.
For instance:

When you re-run your generation script, it can simply skip the “< START MANUAL SECTION >” section, or even append updates below it.

This keeps everything in one file, preserving the single-source-of-truth principle while ensuring your manual context isn’t lost.

Track Update Metadata

A header is a short block of comments at the top of your file that describes when and how it was last updated.

Git history already tracks when a file changed and who changed it — but a header adds context that Git can’t easily show. It’s visible right in the file, even outside the repo, and clarifies how the file was created or maintained.

Use a header when:

• The file is AI-generated or partially automated and you need to note the script or version.

• You have manual sections that shouldn’t be overwritten by regeneration.

• The file may be shared or viewed outside Git, like in code reviews or documentation.

If your context files always live in Git and your team’s PRs are well-documented, you can skip it.
Otherwise, a small header provides quick provenance and prevents confusion — all in just a few tokens.

Customize Your Automation

In many cases, a cookie-cutter generation tool will work just fine, especially for smaller or more straightforward projects. These tools can quickly produce a usable starting point and help keep your context files reasonably up to date with minimal setup.

However, if you’re working with a complex codebase or unique workflow, it’s worth investing in your own custom scripts. A tailored script can understand your project’s specific structure, naming conventions, and which sections should remain untouched or manually maintained.

The goal is to automate around your workflow, not force your workflow to fit someone else’s automation.

At Bitovi, we’ve developed a custom prompt chain for generating a copilot-instructions.md file (a powerful type of context file used with GitHub Copilot). This is a good starting point for building your own customized context generation tool.

When to Update a Context File

Not every code change requires a context update. The right time to refresh a file is when something changes how the system works or how the AI should reason about it.

You should update when:

• The project structure or architecture changes.

• Major components, APIs, or dependencies are added or removed.

• Coding conventions, naming, or design patterns shift.

• The AI starts behaving incorrectly or referring to outdated conventions.

• Documentation or process updates introduce new truths worth reflecting on.

You can usually skip updates for:

• Minor bug fixes or internal refactors that don’t alter behavior.

• Cosmetic documentation tweaks that don’t change meaning.

Think of it this way:
If a human developer needs to re-learn something new about the project, the AI probably does too.

Integrating Context Maintenance into Your Workflow

The best context maintenance is the kind that happens automatically as part of normal work.
You don’t need a new ceremony, just a few smart touchpoints.

• Pull Request Awareness
  Add a checkbox in your PR template:

  “Does this change require a context update?”
  This simple question keeps the habit alive.

• Release Cycle Review
  During release planning or retros, skim your context files.
  If your README or documentation needs an update, your AI context probably does too.

• Automated Regeneration Step
  If you already have a generation script, integrate it into CI/CD.
  Run it nightly or after merges, but have it flag diffs for review rather than auto-committing changes.
  Humans should still approve context updates.

• Developer Feedback Loop
  Encourage your team to log instances where Copilot or your AI assistant “gets it wrong.”
  Those errors are often clues that the context has drifted.

These small touches help maintenance happen continuously instead of as a big, painful cleanup later.