Prompt Engineering Techniques for Better RAG

Fixing RAG Hallucinations

Retrieval-Augmented Generation (RAG) is powerful, but it's prone to hallucinations if the LLM isn't properly constrained. The secret to a reliable RAG pipeline isn't just better embeddings; it's better prompt engineering.

1. Strict Context Constraints

Always instruct the LLM to *only* use the provided context.

"Answer the user's question based strictly on the provided context. If the answer is not contained within the context, politely state that you do not know."

2. Chain-of-Thought (CoT)

Force the model to explain its reasoning before providing the final answer. This dramatically reduces logical errors.

"Let's think step by step. First, analyze the context for relevant information. Second, formulate the answer based on that information."

3. ReAct (Reasoning + Acting)

For AI agents that use external tools, the ReAct framework is essential. It teaches the model to observe, reason, and act in a loop until the goal is achieved.

By mastering these prompt engineering techniques, you elevate your RAG applications from unreliable prototypes to production-grade tools.