Retrieval-Augmented Generation (RAG) Architecture

1Concept

RAG enhances LLM prompts with private enterprise documents: 1. User submits question; 2. Question is converted to embedding; 3. Vector DB retrieves relevant document chunks; 4. Retrieved context is injected into LLM prompt; 5. LLM generates grounded answer without hallucination.

2Architecture Diagram

User Query ---> [ Embed ] ---> [ Vector DB Search ] ---> Relevant Docs
                                                              |
User Query + Relevant Docs Injected ---> [ LLM ] ---> Grounded Fact-Based Answer!

3Code Example

rag_pipeline = '''
def rag_generate_answer(user_query: str, vector_store, llm_client) -> str:
    # 1. Embed query and retrieve top-3 relevant context chunks
    context_chunks = vector_store.search(user_query, top_k=3)
    
    # 2. Inject context into prompt template
    augmented_prompt = f"""
    Context:
    {context_chunks}

    Question: {user_query}
    Answer based strictly on the provided context.
    """
    
    # 3. Generate grounded answer via LLM
    return "Grounded RAG Response"
'''
print("=== End-to-End RAG Architecture Pipeline ===")
print(rag_pipeline.strip())

4Expected Output

=== End-to-End RAG Architecture Pipeline ===
def rag_generate_answer(user_query: str, vector_store, llm_client) -> str:
    # 1. Embed query and retrieve top-3 relevant context chunks
    context_chunks = vector_store.search(user_query, top_k=3)
    
    # 2. Inject context into prompt template
    augmented_prompt = f"""
    Context:
    {context_chunks}

    Question: {user_query}
    Answer based strictly on the provided context.
    """
    
    # 3. Generate grounded answer via LLM
    return "Grounded RAG Response"

5Key Takeaways

• ✓RAG eliminates hallucinations by grounding answers in verified internal documents.
• ✓Chunking strategy (chunk size ~512 tokens with 50 token overlap) is critical for retrieval quality.
• ✓Re-ranking models (Cohere Rerank) refine initial vector search results.