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RAG Engineering AI Engineering

The Complete RAG & Information Retrieval Guide for 2026

A reliable source of knowledge for decision makers, engineers and product managers on Retrieval Augmented Generation and its applications in Agentic AI.

Vstorm Engineering · September 1, 2024 · 12 min read

Build RAG that retrieves
the right thing

Most RAG pipelines fail at retrieval, not generation. The patterns in this guide come from 30+ live deployments across finance, healthcare, real estate, manufacturing, and print-on-demand.

  • Build production RAG systems that don't confuse similar products, services or documents.
  • Solve the accuracy vs. speed trade-off with patterns proven on 30+ live deployments.
  • Pick the right model size and know when smaller models actually outperform bigger ones.
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Why this guide

Your trusted source of ground truth

Stay informed on RAG applications in Agentic AI in business workflows to pick your solutions wisely, gain reliable knowledge on what is possible with modern AI systems, and make informed decisions on your development plan.

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Agentic AI implementations in production
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Engineer-authored — no AI filler, no fluff
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Vertical lenses: finance, healthcare, real estate, manufacturing, print-on-demand
What's covered

Seven chapters from
foundations to production

  1. 01 Foundations & vector retrieval
  2. 02 Disambiguation patterns
  3. 03 Accuracy vs. speed trade-offs
  4. 04 Model selection & cost
  5. 05 Evaluation & metrics
  6. 06 RAG-readiness audit
  7. 07 Production case studies
What's inside

Learning outcomes

  • How to make sure your systems never confuse similar products or services.
  • How to solve the accuracy vs. speed trade-off in real workloads.
  • What the true costs of bigger models are — and when smaller ones win.
  • How to evaluate your solution and set the right metrics to monitor performance.
  • If your systems are RAG-ready — and if not, how to make them so.

70% of RAG failures trace back to chunking strategy, not the language model. Fix the data pipeline before you blame the model.

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Pitfalls & Decisions

Questions worth answering before you build

What chunk size should I start with? +
Start with 512 tokens and 10% overlap. Benchmark Context Recall@5 on your eval set. Below 75% recall, try 256-token chunks. Chunk size is corpus-dependent — there is no universal answer, only the right benchmark for your data.
Do I need a vector database or can I use Postgres? +
For under 500K chunks, pgvector with HNSW index is production-ready and operationally simpler. At 1M+ chunks or under 100ms latency requirements, dedicated stores like Qdrant or Weaviate win. Don't over-engineer before you have a production load.
When should I fine-tune my embedding model? +
When Context Recall is stuck below 70% after chunking improvements. You need at least 1,000 labeled (query, relevant_doc, irrelevant_doc) triplets. Use sentence-transformers with MultipleNegativesRankingLoss. Typical improvement: +15–30% recall.
What's the fastest way to catch hallucinations? +
Add RAGAS Faithfulness as a CI gate: if LLM-as-judge marks more than 10% of answers as unsupported by retrieved context, the build fails. For live monitoring, sample 5% of production queries. Don't rely on user complaints — they see the symptom, not the root cause.
Should I use a reranker? +
Almost always yes. A cross-encoder reranker (bge-reranker-large, Cohere Rerank) adds 50–80ms latency but consistently improves MRR by 15–25%. The one case to skip it: K ≤ 3 results and a very tight latency budget. Retrieve 20, rerank to 3.
VS
Written by
Vstorm Engineering
Agentic AI Engineering Team

Vstorm specialises in production Agentic AI for mid-market companies. 30+ live deployments across finance, healthcare, real estate, manufacturing, and print-on-demand.

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