Content Generation Checklist for AI & Machine Learning

Map each content brief to a concrete audience segment such as MLEs, research engineers, or founders, then specify search intents like evaluation benchmarks, pipeline patterns, or deployment strategies. Document jobs-to-be-done, pain points, and example queries to shape prompts and structure.

Scan GitHub, MLflow runs, and Weights & Biases reports for charts, metrics, and code snippets that can anchor your narrative. Use repository search, nbconvert, and simple grep scripts to surface canonical examples and reduce redundant work.

Export structured results from Jupyter notebooks using Papermill or nbclient and serialize them to JSON for downstream templating. Pull pipeline outputs from Airflow, Prefect, or Dagster task logs and store them in a queryable store like DuckDB or Postgres.

Maintain a bibliography of DOIs, arXiv IDs, and dataset links for every claim or metric, using Zotero or the Semantic Scholar API. Store citations in YAML or BibTeX and tag them to content sections for automated insertion and validation.

Define primary metrics such as newsletter signups, demo requests, or time-on-page, and capture event schemas in analytics. Map each objective to a specific CTA placement, headline variant, and keyword cluster to guide experimentation.

Build clusters around core areas like supervised learning, generative models, evaluation, and MLOps, then attach search terms from Ahrefs or Google Keyword Planner. Tie each cluster to product capabilities and reference assets to ensure depth and relevance.

Chunk and embed content using sentence-transformers, then store vectors in FAISS or Chroma with rich metadata such as section headings and citation IDs. Use LlamaIndex or LangChain retrievers to expose a consistent RAG layer for prompts.

Convert diagrams and tables to deterministic assets using Pandoc and csvkit, and render charts with seeded Matplotlib or Altair scripts. Wrap runnable examples in pytest or papermill pipelines so outputs are reproducible and easily embedded.

Query MLflow or Weights & Biases for final metrics, confidence intervals, training curves, and config diffs, then export structured summaries. Use these artifacts to populate comparison tables and performance claims directly in templates.

Apply Great Expectations to verify schema, ranges, and nulls in data-driven content inputs, and use Evidently to check for drift when pulling live metrics. Fail builds on violations so content does not publish stale or inconsistent numbers.

Create a YAML glossary mapping preferred terms and abbreviations to canonical forms, including model names, metrics, and dataset references. Enforce consistent spelling and capitalization via a linter step that runs before generation.

Track datasets and large artifacts with DVC and images with Git LFS, and pin exact commit SHAs in content manifests. This ensures you can rebuild articles exactly as published and trace where each number came from.

Define strict section structures for blog posts, landing pages, and release notes with placeholders for metrics, citations, and code blocks. Keep templates short, composable, and parameterized to support A/B layout tests.

Use templating to insert persona, topic, claims, and structured facts, and include hard constraints like required sections and token budgets. Add instruction prompts that demand inline citations with IDs that match your bibliography store.

Specify a JSON Schema or Pydantic model for each content type, including allowed tags, section order, and field types. Parse model outputs strictly and fail fast on validation errors to prevent malformed pages.

Pair a general-purpose LLM for narrative with a code model for snippet generation and a vision model for alt text on figures. Set deterministic parameters and normalize sampling seeds, and document model versions and context limits.

Allow the pipeline to call Python for table formatting and Altair or Matplotlib for figures, passing fixed seeds and serialization settings. Persist PNG or SVG assets with content hashes so caches are valid across runs.

Use promptfoo, LangSmith, or HumanLoop to create scored evaluations for factuality, coverage, and style adherence, with thresholds that block publish steps. Include unit tests that verify required claims resolve to retrieval sources.

Build a pipeline in Airflow, Prefect, or Dagster with tasks for ingest, draft, fact-check, render, and deploy, and encode explicit inputs and outputs for each node. Visualize dependencies so failures are isolated and retries remain idempotent.

Drive runs with Hydra or environment-specific YAMLs to vary persona, topic, keyword set, and target channel. Run matrix sweeps for headlines and CTAs while keeping a single code path and consistent seeding.

Cache retrieval results, outlines, and figures keyed by a stable hash of inputs using S3 or MinIO, with metadata in DuckDB. Short-circuit downstream steps when nothing changed to control costs and latency.

Log prompt templates, model identifiers, and data fingerprints to MLflow or Weights & Biases with a link to the Git commit. Store rendered artifacts and evaluation scores as run artifacts for full lineage.

Use exponential backoff and request deduplication keyed by inputs, and place strict budget caps per run. Persist step states so a retry reuses validated outputs rather than regenerating from scratch.

Set up GitHub Actions or GitLab CI to run schema validations, linting, prompt tests, and link checking on every change. Schedule weekly rebuilds for evergreen pieces that depend on dynamic metrics or benchmarks.

Run a pass that extracts claims and verifies them against your retrieval index, matching each statement to a citation ID or experiment artifact. Fail the pipeline if a nontrivial claim lacks a source or mismatches a metric.

Use RAGAS or LangSmith evaluations to quantify answer faithfulness and context precision, and include an LLM-judge step that flags unsupported assertions. Set thresholds that must be met before rendering and publish steps proceed.

Scan for duplication with services like Copyscape, and validate that images, diagrams, and datasets adhere to their licenses, recording attribution strings. Build a preflight step that injects required attributions into captions and footers.

Generate alt text with a vision model and lint it for concision, ensure heading hierarchy is valid, and run Lighthouse and axe checks. Add schema.org Article or FAQ markup and Open Graph tags to boost discoverability.

Render structured drafts to Markdown, HTML, and PDF with Pandoc or Sphinx, and publish to a static site on Netlify or Vercel via CI. For docs sites, update MkDocs or Docusaurus navigation and run link-checkers before deployment.

Track page performance with GA4 and Search Console, and route events through Segment to a warehouse for cohort analysis. Schedule headline and CTA experiments, then feed conversion deltas back to prompt templates as features.