Optimizing RAG at Scale: Chunking Strategies, Hybrid Retrieval & Bayesian Search

Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

Imus rebuilt a production RAG retrieval layer from first principles after naive approaches failed under load. Key result was a p95 latency reduction of 62%—from 850ms down to 320ms—while improving recall@10 by 17 percentage points.

Why This Matters

Most teams ship RAG with fixed token chunks and single-modality embedding search—fine for demos but dangerous in production where legal documents get split mid-clause or API documentation drowns signal in noise. Without structured evaluation and adaptive retrieval, hallucination rates can hit over 12%, cost per query exceeds $0.008 for mediocre answers, and user trust erodes quickly.

Key Insights

• Fixed-token chunking fails on heterogeneous content; recursive clause-aware chunkers improve recall@10 on legal contracts to over 94% versus ~78% baseline.
• Hybrid retrieval combining BM25 exact matching with vector similarity plus cross‑encoder rerank achieves correlation with relevance of ≈0.92 vs bi‑encoder’s ≈0.75.
• Query expansion using an LLM generates three related sub-queries per user question—lifting union recall@10 from single‑query’s ~78% up to ~96%, albeit tripling embedding calls.
• The Pareto frontier found via Bayesian optimization yields three configuration regimes—conservative (91%/180ms), balanced (95%/320ms), aggressive (97%/580ms)—letting teams trade off latency against accuracy per use case.
• A golden dataset versioned in Git serves as both regression test suite and objective function for hyperparameter tuning; automated eval runs enforced by CI prevent regressions.

Working Examples

Abstract base class for document chunking strategies with concrete implementations including FixedTokenChunker.

# rag/chunking.py
from abc import ABC, abstractmethod
from dataclasses import dataclass

@dataclass
class Chunk:
    text: str
    metadata: dict
    token_count: int
    chunk_id: str

class ChunkingStrategy(ABC):
    @abstractmethod
    def chunk(self, document: str, metadata: dict) -> list[Chunk]: ...

class FixedTokenChunker(ChunkingStrategy):
    """Baseline.
    Good for homogeneous content.
    """
    def __init__(self,
                 chunk_size=512,
                 overlap=50):
        self.chunk_size = chunk_size
        self.overlap = overlap

(Cleaned) Implementation of hybrid retriever using Reciprocal Rank Fusion then cross‑encoder re-ranking – core performance win.

# rag/retrieval.py
class HybridRetriever:
    def __init__(self,
                 vector_store,
                 bm25_index,
                 reranker,
                 weights=(0.​4, 0.​3, 0.​3)):
        self.​vector = vector_store
        self.bm25 = bm25_index
        self.reranker = reranker
        self.weight​s = weights # vector,bm25,réranker‍ 💥⚡\nn     async def retrieve(self, query: str,k=20,final_k=5):\nn         # stage1 parallel\nn         vec_results = await self.​vector.search(query,k=k)\nn         bm25_res = await self.bm25.search(query,k=k)\nn         # stage2 rrf fusion\nn         fused = self._rrf(vec_results,bm25_res,k=50)\nn         # stage3 cross encoder rerank top50 →top5\nn         reranked= await self.reranker.rerank(query,fused[:50])\nn         return reranked[:final_k]

Practical Applications

• For high‑throughput APIs serving many concurrent users → deploy conservative config delivering <180 ms p95 while maintaining ~91 % recall.
• When handling high‑stakes legal or medical queries where accuracy matters more than speed → aggressive config yields ~97 % recall despite ~580 ms latency.
• For customer support ticket analysis → use semantic + conversation turn aware chunks together with BM25 fallback so both intent synonyms AND literal error codes match correctly.

References:

• https://dev.to/imus_d7584cbc8ee9b0336256/optimizing-rag-at-scale-chunking-retrieval-and-the-bayesian-search-that-cut-latency-40-2f68