Building Effective RAG Systems

Retrieval Augmented Generation (RAG) combines the power of semantic search with LLMs to ground responses in factual, up-to-date information.

Why RAG?

LLMs have knowledge cutoffs and can hallucinate. RAG solves this by:

• Providing current information
• Grounding responses in your data
• Reducing hallucinations
• Enabling citations

Core Components

1. Document Processing

def chunk_document(text, chunk_size=500, overlap=50):
    """Split document into overlapping chunks."""
    chunks = []
    start = 0

    while start < len(text):
        end = start + chunk_size
        chunk = text[start:end]
        chunks.append(chunk)
        start = end - overlap

    return chunks

2. Embedding Generation

Convert text chunks into vector representations:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')

def embed_chunks(chunks):
    return model.encode(chunks)

3. Vector Storage

Store embeddings in a vector database for efficient similarity search.

4. Retrieval & Generation

def rag_query(question, top_k=3):
    # Embed the question
    q_embedding = model.encode([question])

    # Find similar chunks
    results = vector_db.search(q_embedding, top_k=top_k)

    # Build context
    context = "\n".join([r.text for r in results])

    # Generate response
    prompt = f"Context: {context}\n\nQuestion: {question}"
    return llm.generate(prompt)

Best Practices

1. Chunk wisely - Respect semantic boundaries
2. Rerank results - Use a cross-encoder for better relevance
3. Cite sources - Always attribute retrieved information
4. Handle failures - Graceful fallbacks when retrieval fails

Conclusion

RAG is essential for building trustworthy LLM applications. Start simple, measure quality, and iterate.