RAG in Practice

When large language models entered mainstream business consciousness, their tendency to invent facts presented an immediate challenge. The technical community quickly coalesced around Retrieval-Augmented Generation: rather than relying on a model's static internal memory, a system would first retrieve relevant context from internal databases, inject those documents into the prompt, and instruct the model to answer using only the provided information.

In theory, RAG promised to eliminate hallucinations, ground responses in verifiable truth, and provide instant access to private data without costly retraining. In practice, building a production-grade RAG pipeline is one of the most deceptively complex engineering tasks in modern software.

Where naive RAG fails

Chunking fragmentation is the first problem. Slicing a document at fixed character limits regularly cuts vital sentences in half, stripping context from key facts. Semantic drift in embeddings is the second: vector models match overall topic similarity but miss specific modifiers. A search for contracts without liability caps often retrieves documents dense with liability caps, because the semantic concepts overlap.

The "lost in the middle" problem is the third: models pay heavy attention to information at the beginning and end of a context window, routinely ignoring facts buried in the middle.

Advanced chunking

Modern architectures abandon arbitrary character splits. Parent-child chunking uses small, focused text chunks for precise vector search, but passes the larger surrounding parent document to the LLM for context. Semantic chunking calculates document boundaries dynamically based on shifts in subject matter.

Hybrid search and reranking

Production systems combine dense vector search with sparse keyword engines like BM25. Dense search captures conceptual meaning; BM25 captures exact terms, part numbers, and proper nouns. Merging via Reciprocal Rank Fusion ensures both broad semantic intent and exact factual details are retrieved.

After initial retrieval yields a broad candidate set, a secondary reranking model evaluates precise relevance of each document. The system selects only the top 3 to 5 highest-quality contexts, reducing noise and cost.

The central lesson

The quality of a generative response is strictly bounded by the precision of the underlying retrieval pipeline. Increasing the size of the language model is no substitute for clean, well-indexed data and sophisticated context selection. Accurate intelligence depends on excellent context.