Back to blog
10 min readNgusa

Understanding RAG: Retrieval Augmented Generation Explained

RAG (Retrieval Augmented Generation) has become essential for building context-aware AI applications. Let me break down how it works and how to implement it effectively.

What is RAG?

RAG combines two powerful concepts:

  • Retrieval - Finding relevant information from a knowledge base
  • Generation - Using LLMs to create responses based on retrieved context

The RAG Pipeline

1. Document Processing

python
from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
chunks = splitter.split_documents(documents)

2. Embedding & Storage

python
from langchain.vectorstores import ChromaDB
from langchain.embeddings import OpenAIEmbeddings

vectorstore = ChromaDB.from_documents(
    documents=chunks,
    embedding=OpenAIEmbeddings()
)

3. Retrieval & Generation

python
from langchain.chains import RetrievalQA

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(),
    return_source_documents=True
)

Best Practices

  • Chunk Size Optimization - Balance context vs. specificity
  • Hybrid Search - Combine semantic and keyword search
  • Reranking - Improve relevance of retrieved documents
  • Metadata Filtering - Add structured filters to queries

Common Use Cases

  • Document Q&A systems
  • Knowledge base assistants
  • Customer support chatbots
  • Research assistants

Conclusion

RAG bridges the gap between static LLMs and dynamic, up-to-date information, making it invaluable for production AI applications.

References & Further Reading

    Understanding RAG: Retrieval Augmented Generation Explained | Samwel Ngusa