Enterprise RAG in 2026: practical implementation guide

If your team uses ChatGPT or Claude but feels it "doesn't know your context" — asks over and over about your processes, contracts, policies, ops manual — the technical answer is RAG: Retrieval-Augmented Generation.

A RAG system fetches relevant fragments from your docs and injects them into the model prompt. Done well saves 20-40% of an employee's time on search+drafting. Done poorly burns money on tokens without improving accuracy.

Minimal architecture

Docs → Chunking → Embeddings → Vector Store → Retriever → Reranker → LLM → Answer

The 6 decisions

1. Chunking: fixed 500 tokens with 15% overlap is baseline.
2. Embedding: Cohere multilingual-v3 for Spanish; OpenAI 3-large as fallback.
3. Vector store: pgvector for 90% of cases; Qdrant if scaling.
4. Retriever: top-K=10, hybrid search (vector + BM25), query rewriting.
5. Reranker: Cohere Rerank — highest ROI improvement (+20-30% precision).
6. LLM: GPT-4o-mini for 70% of cases; Claude Sonnet 4.5 for complex reasoning.

Evaluate with RAGAS

Context Recall, Context Precision, Faithfulness, Answer Relevance. Need 30-50 annotated Q&A dataset.

The RAG stack that pays: pgvector + Cohere multilingual-v3 + Cohere Rerank + GPT-4o-mini for 90%, Claude Sonnet when reasoning needed. Cost for 100-person company: ~$150-400 USD/month.

How we help

Our AI Consulting practice does design, 3-4 week POC, RAGAS evaluation, and technical handover. Aligned with ISO 42001 controls from the start.