RAG • Chapter 1

Introduction to RAG & LLMs

RAG engineering module on Introduction to RAG & LLMs.

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.

Retrieval-Augmented Generation (RAG) is an architectural approach that improves the efficacy of Large Language Model (LLM) applications by leveraging custom data. While base LLMs are powerful, they suffer from knowledge cutoffs and 'hallucinations'. RAG bridges this gap.

Advanced System Mechanics

RAG works by fetching relevant information from an external knowledge base and feeding it to the LLM alongside the user's prompt. This grounds the LLM's response in verifiable facts. The typical RAG pipeline consists of three phases: Ingestion (chunking and embedding documents), Retrieval (finding similar chunks via vector search), and Generation (synthesizing the final answer using the LLM).

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

# Basic RAG Pipeline Conceptualization
def simple_rag(query, vector_db, llm):
    # 1. Retrieve relevant context
    context_chunks = vector_db.similarity_search(query, k=3)
    context = "\n".join(context_chunks)
    
    # 2. Augment prompt
    prompt = f"Context: {context}\n\nQuestion: {query}\nAnswer:"
    
    # 3. Generate response
    response = llm.generate(prompt)
    return response

📝 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.

Next →Text Embeddings & Embedding Models

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# Basic RAG Pipeline Conceptualization def simple_rag(query, vector_db, llm): # 1. Retrieve relevant context context_chunks = vector_db.similarity_search(query, k=3) context = "\n".join(context_chunks) # 2. Augment prompt prompt = f"Context: {context}\n\nQuestion: {query}\nAnswer:" # 3. Generate response response = llm.generate(prompt) return response