Guardrails and RAG Evaluation
Block bad outputs and measure retrieval quality.
LLMs Need Bumpers
Left alone, a model can leak data or go off-topic. Guardrails are checks around the model that keep inputs and outputs inside safe bounds. 🛡️
Validate the Input First
Before calling the model, screen the user message for prompt injection or banned content. An input guard stops bad requests from ever reaching the LLM.
All lessons in this course
- How LLMOps Differs from Classic MLOps
- Version Prompts and Evaluate Outputs
- Trace and Monitor LLM Calls
- Guardrails and RAG Evaluation