RAG System Design for Generative AI: Mastering Indexing, Chunking, and Relevance Scoring

Why RAG Is the Default Choice for Enterprise AI Today

Generative AI models like GPT and Claude can sound smart, but they often make things up. That’s not just annoying-it’s dangerous in customer service, legal docs, or medical support. Enter RAG: Retrieval-Augmented Generation. It doesn’t try to memorize everything. Instead, it looks up facts in real time from your company’s documents, databases, or knowledge bases. This cuts hallucinations by grounding answers in trusted sources. By 2026, 70% of enterprise AI systems use RAG, up from just 25% in 2023. The reason? It works without retraining your LLM. You update your documents, and the AI updates its answers-no engineers needed to retrain a 70-billion-parameter model.

How Indexing Turns Documents into Searchable Vectors

Indexing is where RAG starts. You feed in PDFs, wikis, CRM notes, or even Excel sheets. But LLMs don’t read text like humans. They see numbers. So an embedding model-like text-embedding-3-large or all-MiniLM-L6-v2-turns each chunk of text into a vector. Think of it as a fingerprint for meaning. That vector gets stored in a vector database like Pinecone, Weaviate, or Milvus. When someone asks a question, the system turns their words into a vector too. Then it finds the most similar ones in the database. The closer the match, the more relevant the document. Hybrid search is now standard: combine semantic vectors with keyword matching. Google Cloud found this boosts recall by 28%. A query like “How do I reset my password?” might not match the exact phrase in your help doc, but if the doc talks about “account recovery” and “login issues,” hybrid search still pulls it up.

Chunking Isn’t Just Splitting Text-It’s Strategic

Chunking sounds simple: break big documents into smaller pieces. But get it wrong, and your RAG system fails. Too big? You get irrelevant context. A 2000-token chunk about product returns might include one line about shipping delays, but the LLM latches onto that and gives a wrong answer. Too small? You lose context. A 50-token chunk of a legal clause might say “Party A shall not…” without the definition of “Party A.” Optimal chunk size? 256-512 tokens for most enterprise docs. But it’s not one-size-fits-all. Technical manuals often need longer chunks to preserve step-by-step logic. Customer emails? Shorter. Confluent’s team found that streaming updates from operational databases in real time keeps chunks fresh. They don’t re-index everything. They only update changed documents. That’s called delta indexing. 68% of enterprises now use it. Without it, your RAG system answers questions based on last month’s policy, not today’s.

Relevance Scoring: The Quiet Hero of RAG Accuracy

Just retrieving the top 5 documents isn’t enough. You need to know which ones actually help the LLM answer correctly. That’s relevance scoring. It’s not magic. It’s metrics. Precision tells you how many of the retrieved docs were useful. Recall tells you how many useful docs you actually found. Teams that ignore these metrics end up with systems that look good on paper but fail in practice. Orq.ai’s 2025 guide recommends tracking both daily. If precision drops below 70%, your chunks are too big or your embedding model is outdated. If recall is low, you’re missing key documents. Some advanced systems now use query rewriting. If someone types “What’s the refund policy for defective laptops?”, the system might rewrite it to “Return process for damaged electronics under warranty” before searching. Step-back prompting helps too: “What are the key factors that determine a refund?” before asking the real question. These tweaks improve accuracy by 15-22% in real deployments.

When RAG Makes Hallucinations Worse

Here’s the scary part: bad RAG can make hallucinations worse. AWS tested this. When the retrieved documents were irrelevant or outdated, the LLM still used them to build answers-sometimes inventing details to fill gaps. In one case, a support bot pulled a 2023 product spec that said “battery lasts 8 hours.” The real spec, updated in January 2026, said “5 hours.” The LLM didn’t know the update existed. It just used the old doc and said “8 hours.” Result? Customers returned batteries. That’s hallucination amplification. It’s not the LLM lying. It’s the system feeding it bad info and the model trusting it. Fixing this means two things: strict document governance and confidence thresholds. If the top retrieved doc has a similarity score below 0.82, don’t use it. Flag it for review. Build gates. Only allow answers if confidence is high. Forrester says this is non-negotiable for enterprise RAG.

Real-World RAG: Who’s Using It and How

Finance and healthcare lead RAG adoption. Deloitte’s 2025 survey found 83% of Fortune 500 financial firms use RAG to answer compliance questions from regulators. One bank’s system pulls from 12,000 pages of SEC filings, internal audits, and policy manuals. When a customer asks, “Can I defer my loan payment if I lost my job?”, the RAG system finds the exact clause in their 2025 hardship policy and generates a clear answer. No guesswork. In healthcare, hospitals use RAG to answer clinical questions against updated treatment guidelines. One system in Arizona reduced misdiagnosis-related complaints by 41% in six months. Even manufacturing uses it: technicians ask, “What’s the torque spec for this bolt?” and get the exact value from the latest maintenance manual-no more flipping through PDFs. The common thread? All these systems use real-time indexing, smart chunking, and strict relevance scoring. They don’t just connect an LLM to a database. They engineer the pipeline.

What’s Next: Multimodal RAG and Knowledge Graphs

RAG isn’t stuck on text anymore. NVIDIA’s February 2026 research showed vector indexes can now handle images, charts, and tables. A technician uploads a photo of a broken pump. The system compares it to thousands of labeled images and retrieves the repair manual section with matching symptoms. Microsoft’s Azure AI Studio is testing knowledge graphs-networks of facts linked by relationships. Instead of just retrieving documents, it traces connections: “Battery failure → caused by overheating → due to faulty fan → recall ID 2025-047.” This helps with multi-hop questions like, “Why did the Model X battery fail last month?” Traditional RAG fails here. Graph-based RAG can answer it. The market for RAG tools is projected to hit $4.7 billion by 2027. But the winners won’t be the ones with the fanciest models. They’ll be the ones who mastered indexing, chunking, and relevance scoring.

Getting Started: Three Rules for a Working RAG System

1. Start with clean, well-organized data. Garbage in, garbage out. If your knowledge base has 12 versions of the same policy, fix that first.
2. Test chunking with real queries. Don’t assume 512 tokens is perfect. Run 50 sample questions and see which chunks give the right answers. Adjust size and overlap.
3. Monitor precision and recall daily. Set alerts. If precision drops below 70%, pause deployments. Fix the data, not the LLM.

Don’t chase the latest embedding model. Don’t over-engineer the pipeline. Focus on the basics: good data, smart chunks, and honest scoring. That’s how you build a RAG system that doesn’t hallucinate-and actually helps people.