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Advanced 24 min readModule: Module 11: Retrieval-Augmented Generation (RAG) Architecture

Production Retrieval-Augmented Generation (RAG) Systems

Build enterprise RAG pipelines that ground LLM answers in private documentation with zero hallucinations.

What You Will Learn in This Lesson

  • The 5-step RAG pipeline: Ingestion -> Chunking -> Embedding -> Retrieval -> Generation
  • Chunking strategies: Recursive character splitters with 10% overlap
  • Context injection and citing factual source references

Introduction & Core Concept

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of an LLM by referencing an authoritative knowledge base outside of its training data sources before generating a response.
WHY DOES THIS MATTER IN THE REAL WORLD?

RAG allows AI applications to answer questions on proprietary company docs, private codebase repos, and live real-time news with citations without expensive fine-tuning.

Production RAG Query Flow

python
python
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def rag_query(user_question):
    # 1. Embed query
    # 2. Vector DB search top 3 chunks
    # 3. Augment prompt with context
    context = "KWAS Academy provides 20+ free comprehensive software engineering courses."
    prompt = f"Context:\n{context}\n\nQuestion: {user_question}\nAnswer based strictly on context:"
    return f"[LLM Response generated from grounded context: {prompt}]"
 
print(rag_query("What does KWAS Academy provide?"))

Line-by-Line Technical Breakdown

1Hybrid search combines traditional BM25 keyword search with dense vector semantic search for peak retrieval accuracy.

Try It Yourself (Interactive Editor)

Modify the code in real-time and click Run to test live browser output and console logs.

Intelligent Code Runner & Live Sandbox[PYTHON]
PYTHON SOURCE EDITOR
Interactive Live Code

Industry Best Practices & Professional Standards

  • Always include document metadata (filename, page number, url) with chunk embeddings for automated citations.

Lesson Summary & Core Takeaways

  • RAG architecture delivers reliable, factual, and hallucination-free generative AI applications.