Generative AI enthusiasm is everywhere; governed production deployments are not. Security, legal, and data teams rightly ask hard questions about hallucinations, data leakage, cost, and accountability — questions that demo-grade pilots rarely answer.
Teams that reach production treat Gen AI as a platform capability with the same rigor as any enterprise system: defined owners, measurable quality, observable behavior, and clear escalation paths.
Start with use cases that tolerate iteration
Internal knowledge assistants, document summarization with citations, and copilots for analysts are strong first targets. Customer-facing autonomous agents should come later, after evaluation harnesses and human review workflows are proven.
Build evaluation before you scale users
Define golden question sets, expected behaviors, and failure modes. Measure accuracy, latency, cost per query, and refusal rates when context is insufficient. Without benchmarks, you cannot detect regressions when models or prompts change.
- Domain-specific test sets reviewed by subject-matter experts
- Automated regression runs on every prompt or model update
- Red-team scenarios for PII, prompt injection, and off-topic requests
Ground responses with governed RAG
Retrieval-Augmented Generation connects LLMs to approved document corpora and structured data. Success depends on chunking strategy, metadata filters, access controls on source content, and citation UX so users can verify answers.
Design for observability and cost control
Log prompts, retrieved context hashes, model versions, token usage, and user feedback. Set per-team budgets and rate limits. Production Gen AI without FinOps visibility becomes expensive quickly at enterprise scale.
Frequently asked questions
- Do we need a vector database for RAG?
- Not always. Many platforms — including Databricks and cloud-native search services — support vector retrieval without a separate database. The choice depends on scale, latency requirements, and existing data platform investments.
- How do we satisfy security review for LLM applications?
- Document data flows, encryption, access controls, audit logging, and human review for high-risk outputs. Align with your existing AI and data governance policies rather than treating Gen AI as an exception.