Building RAG Pipelines: A Practical Engineering Guide

Retrieval-Augmented Generation (RAG) has become the dominant pattern for grounding large language models in enterprise knowledge. Instead of fine-tuning a model on proprietary data — which is expensive, slow, and creates a stale snapshot — RAG retrieves relevant documents at query time and injects them into the LLM context window. The concept is simple, but production-grade RAG pipelines require careful engineering across every stage. The difference between a demo that works on ten documents and a system that handles millions with consistent accuracy is significant. This guide covers the engineering decisions that matter most.

Chunking: The Foundation That Determines Everything

How you split documents into chunks is arguably the single most impactful decision in a RAG pipeline. Chunks that are too small lose context — a paragraph about "the policy" is meaningless without knowing which policy. Chunks that are too large dilute the relevant information with noise and consume precious context window tokens. The naive approach of splitting on a fixed character count ignores document structure entirely. Production systems should use semantic chunking that respects document boundaries: split on headings, paragraphs, and section breaks. Use overlap (typically 10-20% of chunk size) to preserve context at boundaries. For structured documents like contracts or technical documentation, hierarchical chunking maintains parent-child relationships between sections and subsections. Experiment with chunk sizes between 256 and 1024 tokens — the optimal size depends on your document types and query patterns. Always evaluate chunking changes against your ground-truth evaluation set, not intuition.

Embedding Models and Vector Store Architecture

The choice of embedding model directly determines retrieval quality. General-purpose models like OpenAI text-embedding-3-large or Cohere embed-v3 work well for most English-language use cases, but domain-specific fine-tuning can yield significant improvements for specialized vocabularies (medical, legal, financial). Evaluate models on your actual query-document pairs using metrics like NDCG@10 and recall@k, not generic benchmarks. For vector stores, the decision between a dedicated vector database (Pinecone, Weaviate, Qdrant) and vector extensions on existing databases (pgvector for PostgreSQL) depends on scale and operational preferences. At under 10 million vectors, pgvector is often sufficient and avoids adding another database to your stack. At larger scales, purpose-built vector databases offer better indexing algorithms (HNSW, IVF), sharding, and query performance. Always store the original text alongside vectors — you will need it for debugging, re-embedding when models change, and metadata filtering.

Re-Ranking and Hybrid Search

Vector similarity search alone often misses relevant results, especially for keyword-heavy queries or exact-match requirements. Hybrid search combines dense vector retrieval with sparse keyword matching (BM25) to capture both semantic similarity and lexical overlap. Most production RAG systems retrieve an initial candidate set of 20-50 documents using hybrid search, then apply a cross-encoder re-ranker to score each candidate against the query with much higher accuracy. Cross-encoders like Cohere Rerank or open-source models based on BERT process the query and document together, enabling fine-grained relevance scoring that bi-encoders cannot achieve. The re-ranking stage typically reduces 50 candidates to the top 3-5 that are injected into the LLM prompt. This two-stage retrieve-then-rerank pattern consistently outperforms single-stage retrieval in our benchmarks by 15-25% on relevance metrics.

Evaluation: Measuring What Matters

Building a production RAG pipeline is an iterative engineering discipline, not a one-time integration. Every component — chunking, embedding, indexing, retrieval, re-ranking, and generation — offers tuning opportunities that compound into significant quality differences. The teams that succeed treat RAG as a system to be continuously measured and improved, not a feature to be shipped and forgotten. At OKINT Digital, we help organizations design and build RAG pipelines that are not just functional demos, but production systems with robust evaluation, monitoring, and continuous improvement workflows.