RAG Systems That Actually Work: Beyond the Tutorials

The RAG Reality Check

Every AI tutorial shows you how to build RAG in 20 lines of code. Load documents, chunk them, embed them, retrieve, generate. Simple, right?

Then you deploy to production and users complain: "It doesn't find the right information." "The answers are wrong." "It hallucinates." Welcome to the real world of RAG.

Why Simple RAG Fails

Problem 1: Chunking Destroys Context

Most tutorials chunk by character count. This creates chunks that split mid-sentence, separate questions from answers, and lose document structure.

Better approach: Semantic chunking that respects document structure:

def semantic_chunk(document: str) -> list[str]:
    # Split by semantic boundaries
    sections = split_by_headers(document)
    chunks = []

    for section in sections:
        if len(section) > MAX_CHUNK_SIZE:
            # Split large sections by paragraph
            paragraphs = split_by_paragraph(section)
            chunks.extend(merge_small_paragraphs(paragraphs))
        else:
            chunks.append(section)

    return chunks

Problem 2: Embedding Models Aren't Magic

General-purpose embeddings work poorly for domain-specific content. "Quarterly revenue" and "Q4 earnings" should be similar—but generic embeddings might not capture this.

Solutions:

• Fine-tune embedding models on your domain
• Use hybrid search (semantic + keyword)
• Implement query expansion

Problem 3: Top-K Retrieval is Naive

Retrieving the top 5 most similar chunks often misses relevant context spread across multiple documents.

Better approach: Multi-stage retrieval:

1. Broad retrieval: Get top 50 candidates
2. Reranking: Use a cross-encoder to rerank
3. Diversity selection: Ensure coverage across different aspects
4. Contextual expansion: Include surrounding chunks

The Production RAG Stack

Hybrid Search Architecture

class HybridRetriever:
    def __init__(self, vector_store, bm25_index):
        self.vector_store = vector_store
        self.bm25 = bm25_index

    async def retrieve(self, query: str, k: int = 10):
        # Parallel retrieval
        semantic_results, keyword_results = await asyncio.gather(
            self.vector_store.search(query, k=k*2),
            self.bm25.search(query, k=k*2)
        )

        # Reciprocal Rank Fusion
        fused = self.rrf_fusion(semantic_results, keyword_results)

        # Rerank with cross-encoder
        reranked = await self.reranker.rank(query, fused[:k*2])

        return reranked[:k]

Query Understanding

Before retrieval, understand what the user is actually asking:

• Query classification: Is this a factual question, comparison, or synthesis?
• Entity extraction: What specific things are they asking about?
• Query expansion: Add synonyms and related terms

Answer Validation

After generation, validate the answer:

• Citation checking: Can every claim be traced to source documents?
• Contradiction detection: Does the answer contradict itself or sources?
• Confidence scoring: How well-supported is this answer?

Evaluation: The Missing Piece

You can't improve RAG without measuring it. Build an evaluation pipeline:

1. Retrieval metrics: Precision@K, Recall@K, MRR
2. Generation metrics: Faithfulness, relevance, coherence
3. End-to-end metrics: User satisfaction, task completion

Create a golden dataset of queries and expected answers. Run evaluations on every change.

Conclusion

Production RAG requires thoughtful architecture, domain adaptation, and rigorous evaluation. The tutorials get you 60% of the way—the last 40% is where the real engineering happens.