How to Build a RAG Pipeline

Chunking, embedding, retrieval, reranking - the production RAG architecture that doesnt hallucinate.

Retrieval-Augmented Generation is the most widely deployed LLM architecture in enterprise AI. It is also the most commonly built wrong: naive chunking that breaks context, missing reranking that returns irrelevant documents, and no evaluation that lets hallucinations reach production. This guide covers the pipeline that actually works.

No fluff. Production-grade answers from engineers who ship AI into real products.

Why RAG Fails in Most First Implementations

The naive RAG pipeline: chunk by fixed character count, embed, retrieve top-5 by cosine similarity, pass to GPT-4. This works in demos. It fails in production because fixed-size chunking breaks semantic meaning, cosine similarity retrieves topically similar but contextually wrong documents, and without reranking the LLM receives noise alongside signal. Production RAG requires intentional decisions at every stage: chunking strategy, embedding model selection, vector database design, retrieval approach, reranking, and evaluation.

At Valletta Software, we focus on:

Chunking strategy: recursive character splitting with overlap or semantic chunking - never fixed-size only

Embedding models: text-embedding-3-large for quality text-embedding-3-small for cost - benchmark on your domain

Vector databases: Pinecone Weaviate pgvector Qdrant - choose based on hosting and query patterns

Hybrid search: dense (vector) plus sparse (BM25 keyword) retrieval - combine with RRF or cross-encoder reranker

Reranking: Cohere Rerank or cross-encoder - reorder top-20 before passing top-5 to LLM

Metadata filtering: filter by document type date source before vector search - reduces noise dramatically

Evaluation: RAGAS framework - faithfulness answer relevancy context precision context recall

The Evaluation Framework That Tells You If RAG Is Actually Working

You cannot improve what you do not measure. Most RAG pipelines are deployed without a single evaluation metric.

We give you more than just people. We give you top performers who drive results.

RAGAS: open-source RAG evaluation - faithfulness answer relevancy context precision context recall

Faithfulness: does the answer come from the retrieved context - hallucination detection

Context precision: are the retrieved documents relevant to the question - retrieval quality

Golden dataset: 50-200 question-answer pairs from domain experts - ground truth for automated evaluation

AB evaluation: compare chunking strategies embedding models rerankers - data-driven iteration

Production monitoring: log retrieval results and LLM outputs - catch regressions after updates

Human review loop: sample 5% of production queries for human quality assessment - closes the feedback loop

Your team, your direction. We handle the rest. Rates from EUR 45/h.

EU-incorporated in Malta - NDA on day one, full GDPR compliance. Trusted by startups and enterprise teams across 12+ industries.

See AI Agents in Action

Build RAG pipelines, agents, and LLM integrations from day one

Ship AI features 3x faster with AI-native tooling and methodology

Deploy to production - not just Jupyter notebooks and prototypes

Evaluate output quality - hallucination detection, cost optimization, monitoring

How to Build a RAG Pipeline - With Engineers Who Run Them in Production

Forget the hype. We make AI work in the real world.

Our engineers are trained in the latest AI tooling - Copilot, Claude Code, Cursor, LangChain, and vector databases - and use them daily to ship production AI features, not just prototypes.

Choose from a solo dev, mini team, or full squad. All powered by AI and ready to build from day one.

Lets keep it simple.

Our AI engineers have built production RAG pipelines with RAGAS evaluation hybrid search cross-encoder reranking and hallucination monitoring built in from day one.

How we work