Building Guardrails for RAG Applications

Sun, 10 May 2026 00:00:00 +0000

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.

Practical LLM Guardrails: Hallucination, Topic, PII, and Competitor Controls

Tue, 12 May 2026 00:00:00 +0000

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.

Guardrails on Miguel Lameiro | Cybersecurity Blog & Security Writeups

Building Guardrails for RAG Applications

Introduction

Practical LLM Guardrails: Hallucination, Topic, PII, and Competitor Controls

Introduction