AI / LLM Enablement
LLM governance and RAG patterns that reduce risk
Guardrails, retrieval patterns, and operating practices for safer AI adoption.
TL;DR
LLM adoption is safest when governance is explicit and RAG systems are designed for traceability. Guardrails are as important as model selection.
When you need this
- Teams are experimenting with LLMs without clear guardrails.
- RAG implementations are returning unverified or inconsistent data.
- Security and compliance teams need clarity on AI risks.
Key concepts
Governance guardrails: policies and workflows that control access, retention, and vendor oversight.
RAG patterns: retrieval and grounding techniques that ensure outputs are sourced and explainable.
Evaluation routines: consistent checks for quality, safety, and drift.
Common mistakes
- Allowing unrestricted data access for convenience.
- Skipping evaluation and monitoring once a pilot ships.
- Ignoring knowledge base ownership and update cycles.
Practical checklist
- Define acceptable use and access boundaries.
- Set data retention and deletion expectations.
- Design RAG sources with owners and update schedules.
- Establish evaluation routines for accuracy and safety.
- Document prompt standards and review workflows.
Related services
Need AI guardrails?
We can help you design safe and traceable LLM workflows.