Choosing the right rag strategy for large language models

Retrieval augmented generation (RAG) systems depend on how documents are split and retrieved — poor chunking undermines even the most advanced large language models [devto]. Fixed-size chunking breaks text at set intervals, risking sentence splits that lose meaning. Recursive chunking uses delimiters like paragraphs or headers to preserve structure, but increases processing load. Semantic chunking groups text by meaning using embeddings, improving context retention at the cost of latency.

Hierarchical retrieval combines multiple chunking levels, letting models access both granular details and broader context. Structure-aware parsing leverages document formatting — such as Markdown or HTML tags — to guide segmentation, ideal for technical manuals or API docs. Hybrid methods mix recursive and semantic techniques, balancing accuracy and efficiency for complex queries.

Agentic RAG routes queries through decision agents that choose retrieval paths dynamically, while GraphRAG builds knowledge graphs from documents to surface relational context missed by linear chunking. Each method trades off speed, accuracy, and infrastructure demands.

The right strategy hinges on use-case specifics: legal contracts need structure-aware parsing to preserve clause boundaries, while customer support bots may benefit from semantic or hybrid approaches that capture intent across fragmented inputs [devto]. No single method dominates — performance depends on alignment between document type, query complexity, and system constraints.