Content Generation Checklist for AI & Machine Learning

Identify the specific readers you serve (data scientists, ML engineers, research leads) and the content types they need (how-tos, architecture explainers, experiment reports, release notes). This keeps prompts and retrieval conditioned on relevant pain points and decision criteria.

Define targets for organic traffic to ML keywords, time-to-publish, TTR (time-to-review), and conversion events like API signups or whitepaper downloads. Use these thresholds to gate merges in CI and catch regressions in content quality.

Document tone, citation format, code snippet standards, math notation treatment, and acceptable jargon. Tie these rules to prompt templates and linting (Vale, markdownlint) to enforce consistency automatically.

Prioritize content that supports upcoming releases, new model families, or benchmarks. Feed roadmap tags into metadata so orchestration can auto-generate landing pages and deep dives as features ship.

List approved datasets, OSS licenses, privacy constraints, and claim policies. Embed these as guardrail checks and prompt instructions to avoid leaking PII or misrepresenting benchmark results.

Select model families per task (long-form drafting vs. summarization vs. reranking), pin model versions, and define fallbacks when rate limits or outages occur. Record all choices in config for reproducibility.

Use DVC or Git LFS to track whitepapers, docs, PRDs, notebooks, benchmark results, and support tickets. Hash artifacts so RAG retrieval and content outputs can be traced back to exact source versions.

Chunk by semantic boundaries (headings, sections) with 300-600 token windows and 40-80 token overlap. Preserve section titles and figure/table references to improve retrieval precision for technical claims.

Choose high-performing embedding models (e.g., bge-large, text-embedding-3-large) and a vector DB (Pinecone, Weaviate, Chroma) that supports filtering by metadata like model version, dataset, and date. Fix dimensions and normalize storage for deterministic search.

Store doc_id, version, author, commit hash, paragraph index, and URL for each chunk. Use this to generate inline citations and a sources section with exact anchors for reproducibility.

Run Great Expectations or Soda to detect missing metadata, stale versions, PII, and broken links before indexing. Block ingestion on critical failures and alert the content pipeline channel.

Use Airflow or Prefect with idempotent tasks to re-index only changed files and backfill when needed. Emit lineage so you can regenerate only impacted assets on source updates.

Store system prompts and rubrics in Git with semantic versioning and changelogs. Reference prompt versions in MLflow runs so you can reproduce outputs months later.

Set temperature to 0-0.2, top_p to 0.1-0.3, fix model versions, and use seeds where supported. Record stop sequences and max tokens to prevent drift and truncation.

Define required fields for title, abstract, H2s, FAQs, and source citations, then validate with Pydantic or Guardrails. Fail fast in CI if the model output deviates from the schema.

Parameterize persona, product feature flags, benchmark metrics, and target keywords. Populate variables from YAML or a CMS to keep copy consistent across variants.

Fetch top-k chunks with filters (model="v2", dataset="imagenet"), rerank via bge-reranker or Cohere Rerank, and feed citations directly into the prompt. Require at least two corroborating sources for claims.

For each template, maintain golden prompts and expected snippets or rubric scores. Run pytest to catch regressions when prompts, models, or retrieval configs change.

Disallow unverifiable performance claims, speculative timelines, or private roadmap details with pattern checks and policy prompts. Block publishing if violations are detected.

Localize templates and SEO constraints (title length, date formats) per locale. Ensure RAG retrieves language-matched sources to avoid translation artifacts.

Build Docker images that pin OS, Python, and library versions to eliminate environment drift. Include health checks and resource limits for predictable throughput.

Use Prefect, Temporal, or Airflow with exponential backoff, circuit breakers per provider, and concurrency controls. Separate queues for drafting, retrieval, and validation to avoid contention.

Manage API keys in Vault or cloud Secret Manager, rotate regularly, and inject via short-lived tokens. Add policy checks to prevent secrets in logs or prompts.

Use content and config hashes as cache keys so unchanged inputs skip regeneration. Store intermediate artifacts in Redis or S3 to resume on failure.

Emit token counts, cost per 1k tokens, P50/P95 latencies, and error rates to Prometheus/Grafana. Alert when costs exceed budget or latency breaches SLOs.

Run schema validation, style linting (Vale), link checking, and eval thresholds in CI. Block publish if any critical check fails and provide actionable diffs.

Use LLM-as-judge with a deterministic critic prompt to score factuality, clarity, completeness, and SEO alignment against a rubric. Store scores in MLflow to track improvements over time.

Crawl all outbound links, detect 404s or non-canonical redirects, and ensure anchor text matches target claims. Fail publishing if required citations are missing.

Run content through Presidio or similar detectors and secret scanners before approval. Redact or replace sensitive strings and log incidents for audit.

Compare new outputs to approved baselines for drift in tone, technical accuracy, and structure. Alert when diffs exceed thresholds or critical claims change.

Route drafts to a domain reviewer with checklists for correctness and compliance. Capture feedback and feed it back into prompt and retrieval improvements.

Track engagement, bounce, SERP position, and issue reports. Provide a one-click rollback to the previous version if regressions or errors are detected.

Create title and meta descriptions within character limits and OpenGraph/Twitter cards with proper images. Validate with a linter to avoid truncation and rendering issues.

Add Article or FAQPage structured data with canonical URLs to improve rich results. Validate with Google Rich Results Test in CI.

Programmatically insert links to pillar pages and related tutorials, ensuring no orphaned content. Use topic and entity tags from the corpus to drive link selection.

Use LaunchDarkly or Optimizely to split-test titles and call-to-actions, and define minimum sample sizes for statistical power. Stop underperforming variants quickly to save budget.

Instrument events like API key creation, demo requests, or docs signups and tie them back to the last-touch asset. Feed results into your topic prioritization and prompt tuning.