Maintaining documentation

Unit

In Progress

Imagine trying to retrain a model using instructions written two years ago — half the dependencies no longer work, the dataset path has changed, and nothing runs as expected.

Out-of-date documentation is often more harmful than none at all. It creates false confidence, leading teams to follow bad instructions that waste time, break systems or trigger compliance risks.

Treating documentation like code

Documentation is most reliable when it’s managed the same way as your code — versioned, reviewed and updated as part of the development process.

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Version control:

Store docs in the same repository as code using Git. This ensures updates happen together and drift is minimized.

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Documentation as code:

Use tools that generate documentation directly from code (Sphinx, docstrings, Jupyter annotations) to reduce effort and increase accuracy.

Code and documentation in sync

When documentation is treated like code — stored, tracked and generated alongside it — it stays accurate, evolves with your project and becomes a dependable resource for every team member.

Practical steps for maintenance

Keeping documentation useful isn’t about writing more — it’s about keeping it current.

1. Regular reviews

Schedule recurring checks during sprint reviews or project milestones. Treat documentation reviews with the same seriousness as testing code.

2. Integrate into the workflow

Make updates part of your PR process. Add a checklist item: ‘Have you updated the README or model card if anything changed?’

3. Retain critical information

Capture trade-offs, assumptions and design choices directly. Simple notes like ‘Feature X excluded due to multicollinearity’ save future teams from repeating mistakes.

Action item: Documentation maintenance quiz

You’ve explored why keeping documentation current is critical. Now, test your understanding with a few real-world scenarios.

During a sprint review, your team discovers that the pipeline documentation hasn't been updated in months. Engineers have quietly adapted scripts on their own, but compliance officers are now flagging discrepancies between the documented data flow and the one in production. What's the best corrective step?

A) Ask each engineer to update their own notes so the compliance team has more visibility.B) Implement regular documentation reviews as part of sprint retrospectives, ensuring that both technical and compliance requirements are checked.C) Schedule a documentation 'fix day' to bring everything up to date.D) Delay the compliance review until the model's next major update.

A new model deployment fails because the Dockerfile used in production references outdated dependencies. The README had not been updated to reflect recent library changes. Which practice would most effectively prevent this problem in the future?

A) Create a centralised Word document with the most recent dependencies.B) Assign one engineer to manually track all library changes.C) Increase the frequency of code reviews to catch errors earlier.D) Store documentation in the same repository as the code and enforce updates through pull request checklists.

Six months after deployment, a new hire reintroduces a feature that the original team had deliberately removed due to data leakage. Because the rationale was never documented, the model now shows inflated accuracy during training but fails in production. What's the best long-term solution to avoid such issues?

A) Create a larger training dataset to minimise the impact of data leakage.B) Train new hires more thoroughly so they can infer past design choices.C) Document critical decisions, trade-offs and assumptions at the time they are made and store them in a project changelog.D) Build automated alerts to detect when previously excluded variables are reintroduced.

In progress