Research & Analysis Checklist for AI & Machine Learning

Translate the business decision into measurable outcomes, then map them to ML metrics. For example, link reduced manual review hours to precision at a fixed recall, or lower support cost to intent classification accuracy by channel.

Convert research questions into falsifiable statements with numeric targets and time bounds. Example: "RAG will cut hallucination rate by 30% on a 200-sample domain eval set within 2 s p95 latency."

Document data availability, privacy boundaries, latency budgets, and throughput needs up front. These constraints guide architecture choices such as streaming features, quantization, or serverless vs GPU-backed services.

Choose offline metrics, cross-validation folds, and online A/B frameworks alongside minimum detectable effect and power calculations. This avoids underpowered experiments and misinterpreted results.

Include naive heuristics and simple models as baselines, plus an oracle or human-in-the-loop upper bound where feasible. This anchors progress and guards against over-engineering.

Share a one-pager that lists objectives, metrics, data sources, and risks for sign-off by product, infra, and legal. Use a RACI to clarify who approves, informs, and executes.

Build a map of sources, owners, refresh cadence, and transformations using a data catalog like DataHub or Amundsen. Capture lineage from ingestion to features so issues can be traced quickly.

Verify dataset and model licenses (Apache-2.0, CC-BY, LFS restrictions) and ensure GDPR/CCPA consent, residency, and retention policies are honored. Document restrictions on downstream commercial use.

Automate schema, range, and distribution checks with Great Expectations or Evidently and run them in CI. Fail builds on threshold breaches to catch drift and broken pipelines early.

Estimate noise rates with inter-annotator agreement and spot-checks, then test for temporal and target leakage. Use weak supervision (e.g., Snorkel) to cross-validate or denoise noisy labels.

Create time-sliced reports tracking covariate shift (PSI/KL) and label shift across key cohorts. Set alerts for material changes to stay ahead of model degradation.

Track dataset hashes and lineage with DVC, LakeFS, or Pachyderm. Pin exact snapshots in experiments and publish data cards to document provenance and known gaps.

Compare quality, latency, and cost for public APIs (e.g., OpenAI, Anthropic, Cohere) and open-source models (e.g., Llama, Mistral) on a common domain-specific eval set. Standardize prompts and decoding to ensure fairness.

Monitor Papers with Code, HELM, and Open LLM Leaderboard to understand evolving baselines, datasets, and evaluation gaps. Prioritize findings that align with your domain distribution.

Recreate competitor demos and marketing examples using similar inputs and log deviations. Where possible, test under adversarial or edge-case scenarios to validate robustness claims.

Assess SOC 2/ISO 27001 status, data retention and training usage policies, regional endpoints, SLAs, and rate limits. Require explicit statements about fine-tuning data and log retention.

Build cost curves for inference and training including token-based pricing, batch sizes, context length, and caching effects. Simulate peak loads to ensure budget and SLO alignment.

Track EU AI Act risk classifications, NIST AI RMF guidance, and sector-specific rules. Document controls and disclosures required for your use case and update as regulations evolve.

Log parameters, metrics, artifacts, and datasets in MLflow, Weights & Biases, or Neptune with enforced run naming and tags. Make dataset hash and code commit mandatory fields.

Containerize with Docker, lock dependencies (pip-tools/Conda), record CUDA and driver versions, and set deterministic seeds. Store environment manifests alongside runs.

Define search spaces and budgets and use Optuna or Ray Tune with early stopping schedulers like ASHA. Track trial lineage and promote best checkpoints automatically.

Isolate the impact of features, augmentations, architectures, and regularizers via controlled ablations. Use fixed seeds and consistent splits to ensure comparability.

Break down errors by cohorts (time, geography, device, demographic where permitted) and attach example traces. Visualize calibration, confusion matrices, and failure modes per slice.

Run SHAP/LIME or Captum for feature attributions and test fairness metrics like demographic parity or equalized odds where applicable. Document tradeoffs and mitigations clearly.

Store prompts in Git with Jinja-style variables and track model, temperature, top_p, and stop sequences per run. Use a registry or config files to avoid silent prompt drift.

Construct a small but representative evaluation set with canonical answers and edge cases that mirror production. Update per release and protect it from contamination.

Use RAGAS, TruLens, Promptfoo, or OpenAI Evals to compute task-appropriate metrics like factuality, pass@k, and structured output validity. Combine automatic and human review for high-impact cases.

Apply PII redaction with tools like Presidio, add toxicity/off-topic filters, and enforce JSON schemas with Pydantic or Guardrails. Log refusals and escalations for auditability.

Record tokens in/out, p50/p95 latency, cache hit rates, and cost per 1k requests for each prompt-template and model. Use these signals to choose decoding and reranking strategies.

Measure retrieval recall, chunk relevance, and hallucination rate while experimenting with chunking, embeddings, and rerankers. Tie retrieval metrics to downstream answer correctness.

Write a concise memo summarizing objectives, methods, results, and go/no-go rationale, plus an architecture diagram of the proposed system. Keep it self-contained for leadership review.

Populate standardized cards (e.g., Hugging Face format) covering intended use, dataset provenance, metrics by slice, and known limitations. Publish alongside artifacts for transparency.

Parameterize notebooks with Papermill and export to HTML/PDF, then publish key metrics to Superset or Metabase. Link reports directly to the experiment runs that generated them.

Centralize documents, datasets, models, and eval sets in a searchable repo (e.g., Git + Elasticsearch or a wiki). Tag entries with dataset hashes and run IDs for quick retrieval.

Prepare a deployment checklist, SLOs, alert thresholds, and on-call ownership. Integrate with monitoring platforms like Arize, Fiddler, or Evidently for post-deploy drift and performance tracking.

Conduct a blameless retrospective after major milestones to capture what worked, what to automate next, and which templates to update. Share outcomes broadly to improve team velocity.