Module 7: Retrieval-Augmented Generation (RAG)
Master Retrieval-Augmented Generation (RAG) in LangChain — building pipelines, custom prompts, conversational retrieval, advanced search (HyDE, Multi-Query, Parent Document), cross-encoder reranking, and evaluation…
Retrieval-Augmented Generation (RAG) is a design pattern that extends the capabilities of Large Language Models (LLMs) by dynamic grounding with relevant external data. Instead of retraining or fine-tuning models, RAG retrieves relevant document snippets from a knowledge base and inserts them into the LLM prompt context to answer specific user queries. This module covers core RAG, conversation history management, advanced query transformation, reranking, and systematic evaluation.
RAG Architecture & First Pipeline
Why this matters
RAG grounds LLM answers in your data — retrieve relevant chunks, then generate with context.
RAG = retrieve relevant chunks + stuff them into a prompt + generate an answer grounded in sources.
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import FAISS
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
vectorstore = FAISS.from_documents(chunks, OpenAIEmbeddings())
retriever = vectorstore.as_retriever()
# combine docs + LLM into retrieval chain (LangChain 0.2+ pattern)- Reduce hallucinations by citing retrieved context only.
- Custom system prompt: "Answer using ONLY the provided context."
Common mistakes
- Hard-coding API keys in source instead of environment variables.
- Passing raw strings where ChatPromptTemplate expects message tuples.
- Skipping text splitting before embedding large PDFs (context overflow).
Interview checkpoints
- Q: Explain rag pipeline in LangChain. A: One-sentence definition + one API name.
- Q: Common bug? A: Keys, message format, or missing split/embed step.
Practice
- Basic: Sketch a minimal rag pipeline snippet.
- Intermediate: Run a notebook cell demonstrating RAG Pipeline.
- Advanced: Break RAG Pipeline intentionally and interpret the error.
Recap
- You can explain rag pipeline clearly.
- You know one mistake to avoid.
- You see how this connects to the next lesson.
Next: RAG Evaluation
Advanced RAG & Evaluation
Why this matters
Advanced RAG adds reranking, conversational history, and evaluation to reduce hallucinations.
Improve RAG with hybrid search, rerankers, and conversational memory. Evaluate with faithfulness and answer relevance metrics.
ConversationalRetrievalChain— follow-up questions with history.- Common pitfalls: chunks too large, wrong metadata, no eval set.
Common mistakes
- Hard-coding API keys in source instead of environment variables.
- Passing raw strings where ChatPromptTemplate expects message tuples.
- Skipping text splitting before embedding large PDFs (context overflow).
Interview checkpoints
- Q: Explain rag evaluation in LangChain. A: One-sentence definition + one API name.
- Q: Common bug? A: Keys, message format, or missing split/embed step.
Practice
- Basic: Sketch a minimal rag evaluation snippet.
- Intermediate: Run a notebook cell demonstrating RAG Evaluation.
- Advanced: Break RAG Evaluation intentionally and interpret the error.
Recap
- You can explain rag evaluation clearly.
- You know one mistake to avoid.
- You see how this connects to the next lesson.
Next: Agents & Tools