RAG • Chapter 7

Prompt Engineering for RAG

RAG engineering module on Prompt Engineering for RAG.

6 note blocks4 exam topics

🎯 Exam Focus Areas

Evaluate chunking and embedding strategies.Understand Vector DB indexing architectures like HNSW.Analyze RAG prompts for injection vulnerabilities.Calculate and utilize RAGAS evaluation metrics.

The effectiveness of a RAG system heavily depends on how the prompt is structured. The LLM must be explicitly instructed on how to handle the provided context.

Advanced System Mechanics

A robust RAG prompt typically includes: the Persona, the strict instruction to ONLY use the provided context, the Context itself, and a fallback instruction (e.g., 'If the answer is not in the context, say I do not know'). Advanced techniques like 'Chain of Thought' prompting can also be combined with RAG to force the model to explain its reasoning based on the retrieved chunks.

1Understand the vector space implications of this concept.
2Identify potential hallucination risks.
3Optimize for low latency and high relevance.
4Ensure robust system prompts.

Implementation Blueprint

RAG_PROMPT_TEMPLATE = """
You are an expert technical support agent.

Context information is below.
---------------------
{context}
---------------------

Given the context information and not prior knowledge, answer the query.
If you don't know the answer based on the context, just say "I don't have enough information."

Query: {query}
Answer: 
"""

📝 Quick Revision Points

1Review the differences between similarity metrics.
2Practice the LangChain/LlamaIndex code snippets.
3Understand the HyDE architecture deeply.
4Memorize the security guardrail implementations.

← PreviousIntegrating LLMs (OpenAI, HuggingFace)Next →Advanced RAG: HyDE & Parent Document

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RAG_PROMPT_TEMPLATE = """ You are an expert technical support agent. Context information is below. --------------------- {context} --------------------- Given the context information and not prior knowledge, answer the query. If you don't know the answer based on the context, just say "I don't have enough information." Query: {query} Answer: """