What No RAG Tutorial Tells You Before You Start Building

2026-05-06 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

This article provides a practical guide to building robust Retrieval-Augmented Generation (RAG) pipelines, moving beyond basic tutorials that often overlook common production failures. It details the three core stages of RAG: retrieval, augmentation, and generation. The author highlights critical implementation challenges, including the "chunking problem," where initial small chunk sizes (e.g., 200 tokens) lead to lost context, recommending starting with 512 tokens and 15% overlap. The piece also addresses the limitations of pure semantic search for short queries or specific terms, advocating for hybrid search (combining BM25 keyword search and vector search) with a re-ranking step to improve retrieval accuracy and prevent hallucinations. The author emphasizes that retrieval quality is paramount, framing RAG as a shift from making the model "smarter" to providing it with "better information."

Key takeaway

For AI Engineers building production RAG systems, prioritize robust retrieval over solely focusing on LLM prompting. Your initial RAG pipeline will likely fail on real-world data, so start with 512-token chunks and 15% overlap, integrate hybrid search, and always include a re-ranking step. When the system provides confidently wrong answers, view it as a diagnostic signal to refine your retrieval strategy, as this is where most production issues arise.

Key insights

Effective RAG implementation hinges on robust retrieval strategies, not just LLM capabilities, to prevent confident but incorrect answers.

Principles

• Chunk size and overlap are critical for context preservation.
• Hybrid search outperforms pure semantic or keyword search.
• Retrieval quality sets the ceiling for system performance.

Method

Start RAG chunking with 512 tokens and 15% overlap. Implement hybrid search (BM25 + vector search) with a re-ranking step. Limit context to 4-6 chunks for the LLM.

In practice

• Use 512-token chunks with 15% overlap.
• Implement hybrid search for better accuracy.
• Add a re-ranking step for retrieved chunks.

Topics

• Retrieval-Augmented Generation
• RAG Pipeline
• Chunking Strategy
• Semantic Search
• Hybrid Search

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Building RAG Systems: A Complete Guide — RAG systems augment LLMs with external knowledge via structured ingestion and dynamic retrieval pipelines.
• Building a Production-Ready RAG Pipeline Workshop — Building production RAG systems requires advanced techniques beyond basic chunking and embedding.
• What is RAG (Retrieval-Augmented Generation) and How It Works in Real AI Systems — RAG enhances LLM accuracy by integrating external data retrieval, mitigating hallucinations and outdated information.
• Stop Building “Naive RAG” — Here Are the 12 Retrieval Architectures Powering Production AI ||… — RAG is a category, not a single technique; choose architectures based on query and knowledge base characteristics.
• 10 Practical RAG Approaches: What Is Actually Useful and When to Use Each One — Effective RAG implementation requires a phased approach, starting simple and adding complexity only as specific bottlenecks are identified.
• Building a Production-Ready Agentic Chatbot Course [3/30]: Designing Production-Ready RAG Pipeline — A production RAG pipeline requires a multi-stage approach beyond simple vector search to ensure reliable information retrieval.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.