Documentation & Knowledge Base Checklist for AI & Machine Learning

Segment readers into data scientists, ML engineers, MLOps, and product stakeholders, then map top tasks such as training, deployment, experiment review, and incident response. Use task-based navigation and user journeys so critical paths are one click away from the homepage.

Choose Sphinx, MkDocs Material, or Docusaurus with a single repo source of truth, versioned alongside code. Enable fast local preview and CI builds so contributors iterate quickly, and use Algolia DocSearch for fast search across APIs, guides, and notebooks.

Document conventions for tensor shapes, dtype notation, metrics naming, and pseudocode style, plus when to show code versus equations. Enforce with Vale or alex for prose linting and pre-commit hooks to prevent off-spec terms from entering main.

Maintain canonical definitions for AUC, F1, perplexity, calibration, and drift metrics with units and computation formulas. Cross-link with Intersphinx or Sphinx glossary directives so definitions are reused across API docs, model cards, and runbooks.

Publish tested combinations of CUDA, cuDNN, PyTorch or TensorFlow versions, Python versions, and GPU types with known caveats. Provide a quick-start environment file and Docker image references to reduce setup friction and avoid silent ABI mismatch failures.

Use predictable slugs and anchor IDs for cross-referencing sections from code comments, notebooks, and external posts. Enable mkdocs-material instant anchors or Sphinx smart anchors to avoid link rot when titles change during refactors.

Store Architecture Decision Records that explain choices around feature stores, model serving framework, or vector DB selection. Link ADRs from relevant guides so context is preserved for future contributors and audit trails during compliance reviews.

Describe REST endpoints with OpenAPI 3.1, including request and response schemas for batch, streaming, and async jobs. Use Redocly or Stoplight to generate human-friendly docs, and include example payloads with edge cases like ragged sequences and variable image sizes.

Use Sphinx autodoc, pdoc, or mkdocstrings to pull Python and TypeScript signatures from code with typing annotations. Include examples for NumPy arrays, Pandas DataFrames, and PyTorch tensors, and show failure modes when shapes do not match documented contracts.

Explain API keys, OAuth flows, and service accounts with copy-paste curl and requests examples. Publish specific rate limit strategies like token buckets, backoff guidance, and batch aggregation patterns to avoid 429s during large offline scoring jobs.

Bundle a Postman collection and a minimal Jupyter notebook that covers happy path, common errors, and retries. Include a Colab link that runs against a staging environment to validate credentials and shapes before hitting production.

List error codes with remediation steps, including CUDA out-of-memory, model warmup latency, invalid dtype, and schema mismatches. Include links to Grafana dashboards, Sentry traces, or OpenTelemetry traces so responders jump directly into relevant observability tools.

State SemVer rules, supported versions, and deprecation timelines, with Git tags and GitHub Releases linked to docs. Provide migration recipes for breaking changes with code snippets that show before and after for each major client library.

Explain how to choose batch sizes for GPUs, CPU vectorization, and sequence lengths, including throughput vs latency trade-offs. Offer concrete patterns using Ray, Dask, or asyncio to parallelize requests safely under documented rate limits and memory constraints.

Document provenance, consent, sampling, label methodology, and known biases using the Datasheets for Datasets framework. Link to validation reports from Great Expectations or Soda so consumers can evaluate quality and freshness before training or inference.

Expose data types, nullability, and ranges for features stored in Feast or a custom store, including backfill and TTL policies. Use dbt docs or a schema registry to generate browsable lineage that shows upstream dependencies and owners for each feature.

List critical expectations, drift thresholds, and alert routing for broken pipelines, with pointers to GX suites or custom checks. Provide a runbook for failure triage and rollback that includes reprocessing commands and backfill windows.

Create experiment report templates that surface MLflow or Weights & Biases runs, including hyperparameters, seeds, metrics, and artifacts. Link top experiments to model cards and release notes so decision context is preserved beyond the tracker UI.

Include intended use, limitations, datasets, training procedure, evaluation slices, and fairness metrics like demographic parity or equalized odds. Provide reproducibility notes, calibration details, and caveats for out-of-distribution inputs, with references to evaluation notebooks.

Publish conda or pip lock files, Dockerfile references, and RNG seeding strategy, and specify CPU vs GPU parity expectations. Show one command to reconstruct a training run using DVC or git-lfs tracked artifacts and exact data snapshot versions.

For BentoML, Seldon, or TorchServe, provide payload schemas, batch semantics, warmup strategies, and healthcheck endpoints. Include examples for streaming token generation and image preprocessing steps so clients implement the same transforms as training.

Use GitHub Actions or GitLab CI to run Sphinx or MkDocs with strict settings, caching, and link checking. Publish previews with Netlify, Vercel, or GitHub Pages so reviewers see exact artifacts, not screenshots or PDFs.

Adopt Conventional Commits and generate Keep a Changelog style release notes per version, grouping features, fixes, and breaking changes. Publish SDK and model release notes alongside API changes so downstream teams can plan upgrades confidently.

Use MyST-NB or Jupyter Book to execute and render notebooks in CI, pinning seeds and datasets to avoid flaky outputs. Strip or cache cell outputs for heavy models and validate execution with papermill or nbconvert to catch broken examples early.

Adopt Mermaid or PlantUML for pipeline, feature store, and serving architecture diagrams, built automatically in CI. Keep sources in the repo so topology changes are reviewed with code and visuals never drift from reality.

Run vale for style, codespell for typos, and lychee for link checking as mandatory CI steps. Block merges that introduce broken anchors, spelling regressions in API names, or missing examples for new endpoints.

Use Intersphinx inventories or mkdocs monorepo plugins to cross-link API and model docs across services. Pin external reference versions to avoid broken references when upstream projects publish new releases with moved anchors.

Schedule crawls for Algolia DocSearch or update an OpenSearch index after successful builds, including code example snippets and notebooks. Track queries and zero-result searches to identify missing topics and prioritize content gaps in the backlog.

Provide a day 0 setup guide that covers GPU driver installation, Docker, Python toolchains, and access to data warehouses and experiment trackers. Include a 90-minute hands-on exercise that trains a small model and serves it locally with FastAPI.

Document detection thresholds and dashboards from Evidently, Arize, or Fiddler, plus escalation policies and rollback mechanics. Include playbooks to rebaseline metrics, backfill features, or temporarily switch to a champion model with precise commands.

Standardize prompt templates, evaluation datasets, and guardrail policies using LangSmith, Promptfoo, or Ragas. Document versioned prompts, expected outputs, and failure cases like hallucinations or prompt injection with remediation steps and test harness links.

Curate tasks like batch inference with Spark, online feature retrieval with Feast, or vector search with FAISS and pgvector. Each recipe includes prerequisites, code snippets, resource sizing guidance, and links to reusable Terraform or Helm charts when applicable.

Use CODEOWNERS to require review from API, data, and model owners before docs merge. Provide PR templates that ask for examples, benchmarks, and migration guidance to ensure new features are well documented at the time of release.

Explain how to use Vault, AWS Secrets Manager, or GCP Secret Manager for local and CI environments, and how to rotate credentials. Include data masking policies and approved tokenization strategies for logs, notebooks, and sample datasets.

Add privacy-respecting analytics like Plausible or Google Analytics and track search queries, exits, and time to first success. Offer a feedback widget that opens an issue or discussion pre-filled with the page context to streamline content improvements.