RAG Pipelines in Enterprise — How to Do Retrieval-Augmented Generation in Practice

Retrieval-Augmented Generation (RAG) has become the de facto standard for enterprise AI applications that need to work with internal data. But between “works in a demo” and “works in production” is a chasm. How to bridge it?

Why RAG and Why Now¶

Fine-tuning LLM models on company data is expensive, slow, and hard to maintain. RAG offers an elegant alternative: keep the model general and supply relevant context at runtime. In 2026, we have mature embedding models, stable vector databases, and enough production experience to know what works.

Typical enterprise use cases include internal knowledge bases (documentation, wiki, processes), customer support over product documentation, compliance — searching regulations and internal policies, and contract and legal document analysis.

RAG Pipeline Architecture in 2026¶

A modern RAG pipeline has four key phases:

• Ingestion: Processing source documents — parsing, cleaning, chunking
• Indexing: Generating embeddings and storing in a vector database
• Retrieval: Finding relevant chunks based on the query
• Generation: Assembling the prompt with context and generating the answer

Each phase has its pitfalls. Let’s look at them in detail.

Chunking — The Foundation of Success¶

Chunking is the most underestimated part of the RAG pipeline. Bad chunking = bad results, regardless of model quality. These strategies have proven effective in practice:

• Semantic chunking: Instead of fixed length, split text by semantic boundaries — headings, paragraphs, topical units. Requires preprocessing but dramatically improves retrieval quality.
• Overlap with context: 10–20% overlap between chunks ensures information at boundaries isn’t lost. We also add metadata — document name, section, date.
• Hierarchical chunking: Two levels — parent chunks (broader context) and child chunks (detail). Retrieval searches at the child level, but the parent chunk goes into the prompt.

Optimal chunk size depends on the use case. For factual Q&A, typically 256–512 tokens; for analytical tasks, 512–1024 tokens. Always measure on real data.

Embedding Models — Selection and Trade-offs¶

In 2026, we can choose from several embedding model categories:

• OpenAI text-embedding-3-large: Solid performance, simple integration, but data leaves the perimeter
• Cohere embed-v4: Strong multilingual performance, suitable for Czech data
• Open-source (nomic-embed, BGE, E5): You can host on-premise, full data control
• Domain-specific models: Fine-tuned embeddings for a specific domain (legal, medical) — best performance but requires training investment

For Czech enterprise clients, we typically recommend a hybrid approach: open-source model hosted on-premise for sensitive data, commercial API for less sensitive use cases.

Retrieval — More Than Just Cosine Similarity¶

Naive RAG relies on vector similarity. In practice, that’s not enough. A modern retrieval pipeline combines:

• Hybrid search: Vector search + BM25 (keyword search). Fusion algorithm (RRF — Reciprocal Rank Fusion) combines results from both approaches.
• Query transformation: Before searching, transform the query — synonym expansion, decomposition of complex questions into sub-queries, HyDE (Hypothetical Document Embeddings).
• Reranking: A cross-encoder model reranks top-K results from the first round. Slower but significantly more accurate. Cohere Rerank or open-source alternatives (BGE-reranker).
• Metadata filtering: Filtering by date, department, document type — reduces noise and speeds up retrieval.

Vector Databases — Technology Choice¶

The vector database market has consolidated in 2026. Main choices:

• pgvector (PostgreSQL): If you already have Postgres, a great start. HNSW indexes handle millions of vectors. Advantage: one database for everything.
• Qdrant: Rust-based, high performance, good filtering. Popular in the EU for on-premise deployment options.
• Weaviate: Built-in vectorization, GraphQL API, multi-tenancy. Suitable for SaaS platforms.
• Managed services (Pinecone, Azure AI Search): Easiest operations, but data lives on the provider’s cloud.

For most enterprise projects, we recommend pgvector as a starting point — it minimizes operational complexity and most teams already know Postgres.

Evaluation — How to Measure RAG Quality¶

Without systematic evaluation, you don’t know if the RAG pipeline actually works. We measure at three levels:

• Retrieval quality: Precision@K, Recall@K, MRR (Mean Reciprocal Rank) — does the retriever return relevant documents?
• Generation quality: Faithfulness (does the generation match the context?), relevance (does it answer the question?), completeness
• End-to-end: User satisfaction, answer correctness verified by a domain expert

Frameworks like RAGAS automate evaluation using an LLM-as-judge approach. But note — automatic evaluation is indicative. For production systems, regular human evaluation on a data sample is essential.

Common Mistakes and How to Avoid Them¶

• Ignoring preprocessing: Garbage in, garbage out. Invest in data cleaning — removing duplicates, parsing tables, extracting from PDFs.
• Too much context: More chunks ≠ better answers. The “lost in the middle” effect causes the model to ignore relevant information in the middle of long context.
• Missing observability: Log every pipeline step — which chunks were returned, what the confidence score was, what the final prompt looked like.
• Static pipeline: Data changes; the pipeline must reflect updates. Implement incremental indexing and versioning.

RAG Is an Engineering Discipline, Not Magic¶

A quality RAG pipeline requires the same engineering discipline as any other production system. Chunking, embedding, retrieval, evaluation — each step requires measurement, iteration, and optimization on real data.

Our tip: Start with a simple pipeline, measure baseline metrics, then iterate. Most improvements come from better chunking and reranking, not from swapping the LLM model.