Beyond Basic RAG

You've built a basic RAG system—connect an LLM to a vector database, retrieve relevant chunks, and generate answers. But production RAG requires much more sophistication.

The RAG Evolution

RAG has evolved through three distinct generations:

Generation	Approach	Characteristics
Naive RAG	Simple retrieval + generation	Single query, top-k chunks, direct to LLM
Advanced RAG	Pre/post retrieval optimization	Query rewriting, reranking, hybrid search
Agentic RAG	Autonomous reasoning	Multi-step retrieval, self-correction, tool use

Naive RAG Limitations

The basic "retrieve and generate" approach suffers from:

# Naive RAG - what most tutorials teach
def naive_rag(query: str):
    # Single embedding search
    docs = vectorstore.similarity_search(query, k=4)

    # Direct concatenation
    context = "\n".join([d.page_content for d in docs])

    # Hope for the best
    return llm.invoke(f"Context: {context}\n\nQuestion: {query}")

Problems:

• Query-document mismatch (user questions ≠ document style)
• Irrelevant chunks pollute context
• No verification of retrieval quality
• Fixed retrieval regardless of query complexity

Advanced RAG Improvements

Advanced RAG addresses these with systematic optimizations:

# Advanced RAG - production approach
def advanced_rag(query: str):
    # Pre-retrieval: Query enhancement
    expanded_query = query_expander.expand(query)

    # Retrieval: Hybrid search
    semantic_results = vectorstore.similarity_search(expanded_query, k=10)
    keyword_results = bm25_search(expanded_query, k=10)
    fused_results = reciprocal_rank_fusion(semantic_results, keyword_results)

    # Post-retrieval: Reranking
    reranked = reranker.rerank(query, fused_results, top_k=4)

    # Generation with grounding
    return generate_with_citations(query, reranked)

Agentic RAG

The latest evolution adds autonomous decision-making:

• Adaptive retrieval: Only retrieve when needed
• Multi-step reasoning: Break complex queries into sub-queries
• Self-correction: Verify and retry on low-confidence answers
• Tool integration: Search web, databases, APIs as needed

Course Focus

This course focuses on Advanced RAG techniques—the production-ready middle ground between naive simplicity and agentic complexity. You'll learn:

• Embedding model selection and optimization
• Vector database architecture and indexing
• Advanced chunking strategies
• Hybrid search and reranking
• Systematic evaluation with RAGAS

Key Insight: Most production RAG failures aren't model problems—they're retrieval problems. Master retrieval, and your RAG quality follows.

Next, we'll compare RAG against fine-tuning to understand when each approach excels. :::