Rag

Introduction RAG applications are easy to prototype and hard to make reliable. A chatbot can retrieve documents, pass them to an LLM, and answer questions in a few lines of code. The hard part is making sure it does not invent unsupported details, drift off-topic, leak personal data, or violate business rules. This course frames guardrails as a secondary validation layer around LLM inputs and outputs. Prompting, fine-tuning, RLHF, and RAG help, but they do not remove the need for runtime checks. ...

Introduction The guardrails course becomes most useful when it moves from a simple keyword detector to specialized validators. The material covers four practical controls: hallucination detection with Natural Language Inference, topic restriction with zero-shot classification, PII detection with Microsoft Presidio, competitor mention detection with exact matching, NER, and vector similarity. Each control protects a different failure mode. Together they show the right engineering pattern: use small, task-specific models and validators around the LLM instead of expecting the LLM to police itself. ...