Loading page content
Free AI planning resource
Production AI requires more than a prototype: you need a justified use case, reliable data access, architecture choices (API, RAG, agents, or fine-tuning), evaluation, guardrails, and a controlled rollout. This checklist helps teams assess readiness before committing to build.
High-risk domains may require formal risk assessments, DPIAs, model cards, and specialized compliance review beyond a checklist.
Prototypes often skip evaluation, permissions, cost controls, monitoring, rollback, and human review. Production AI must handle edge cases, abuse, privacy, and measurable quality at scale.
When a simpler rule-based or search solution suffices, when data is insufficient or unreliable, when errors carry unacceptable risk without human review, or when cost/latency cannot meet product requirements.
RAG suits knowledge grounded in your documents; fine-tuning suits consistent style or specialized tasks with sufficient training data; agents suit multi-step workflows with tools. Many products combine API calls with RAG and limited agent orchestration.
No system guarantees zero hallucinations. Use retrieval grounding, evaluation, refusal rules, human review for high-risk outputs, and clear user disclosures.
Format: PDF + DOCX
Time to complete: 2–3 hours to complete
Audience: CTOs, product managers, founders, operations leaders, healthcare and SaaS teams