Designing RAG Pipelines for Production
Lessons learned from building retrieval-augmented generation systems that scale reliably under real-world constraints.
The Production Gap
Most RAG tutorials demonstrate a simple flow: embed documents, store in a vector database, retrieve top-k results, and feed them to an LLM. This works in demos. It fails in production.
The gap between a working prototype and a reliable production system is where engineering discipline matters most.
Architecture Decisions That Matter
Chunking Strategy
The single most impactful decision in a RAG pipeline is how you chunk your documents. Too small, and you lose context. Too large, and you dilute relevance.
| 1 | class SemanticChunker: |
| 2 | def __init__(self, model, max_tokens=512, overlap=50): |
| 3 | self.model = model |
| 4 | self.max_tokens = max_tokens |
| 5 | self.overlap = overlap |
| 6 | |
| 7 | def chunk(self, document: str) -> list[Chunk]: |
| 8 | sentences = self.split_sentences(document) |
| 9 | embeddings = self.model.encode(sentences) |
| 10 | boundaries = self.find_semantic_boundaries(embeddings) |
| 11 | return self.merge_at_boundaries(sentences, boundaries) |
Retrieval Quality
Vector similarity alone is insufficient. Production systems need:
- Hybrid search: Combine dense retrieval (embeddings) with sparse retrieval (BM25)
- Re-ranking: Use a cross-encoder to re-score retrieved passages
- Query expansion: Decompose complex queries into sub-queries
Evaluation Framework
You cannot improve what you cannot measure. Build evaluation into the pipeline from day one.
| 1 | class RAGEvaluator: |
| 2 | def evaluate(self, query, retrieved, expected): |
| 3 | return { |
| 4 | class=class="syn-str">"syn-str">class="syn-str">"recall@k": self.recall_at_k(retrieved, expected), |
| 5 | class=class="syn-str">"syn-str">class="syn-str">"mrr": self.mean_reciprocal_rank(retrieved, expected), |
| 6 | class=class="syn-str">"syn-str">class="syn-str">"faithfulness": self.check_faithfulness(query, retrieved), |
| 7 | } |
Scaling Considerations
- Index sharding for large document collections
- Caching frequently accessed embeddings
- Async retrieval with timeout fallbacks
- Version control for embedding models and indices
Key Takeaways
Production RAG is an engineering discipline, not a prompt engineering exercise. Invest in evaluation, chunking strategy, and hybrid retrieval before optimizing prompts.