RAG Architectures at a Glance: Choosing the Right Pattern
March 1, 2025
Language models speak with confidence even when they are wrong. Retrieval-Augmented Generation grounds them in verified sources — but the right architecture depends on the questions you need answered.
Why RAG Matters
Large language models are remarkably capable but share one fundamental limit: they can only repeat what they learned during training. In fast-moving domains — law, medicine, internal company data — that knowledge can be outdated before the model ever reaches production. Retrieval-Augmented Generation (RAG) addresses this by letting the model access fresh, verified documents at query time. RAG does not replace fine-tuning, nor the reverse — they are complementary tools. RAG provides knowledge: access to your data. Fine-tuning shapes style and behaviour. Many production systems combine both. The challenge is that there is no single RAG architecture — there are nine widely recognised patterns, each designed for a different operational reality.
The Nine Architectures in Brief
Standard RAG is the baseline. It retrieves documents via vector similarity and feeds them to the model. Fast, affordable, and ideal for straightforward knowledge bases.
Conversational RAG adds a memory layer so the system maintains context across multi-turn dialogues. Essential for customer support and advisory applications.
Corrective RAG (CRAG) introduces a quality gate that evaluates retrieved documents before they reach the model. When internal data falls short, it automatically consults external sources. Built for high-stakes accuracy.
Adaptive RAG routes queries by complexity — simple questions skip retrieval entirely, moderate ones get a single pass, and complex ones trigger multi-step analysis. Optimises cost and speed.
Self-RAG embeds a real-time critique mechanism into the generation process. The model evaluates its own output as it writes, catching unsupported claims before they surface. Demands specialised models.
Fusion RAG generates multiple reformulations of each query and searches in parallel, then ranks results by cross-query consistency. Maximises recall for ambiguous or poorly worded inputs.
HyDE drafts a hypothetical answer first, then searches for real documents that match it. Bridges the semantic gap between questions and answers for conceptual queries.
Agentic RAG deploys an autonomous agent that plans, executes, and iterates across multiple tools and sources. The most capable pattern for cross-domain, multi-step reasoning.
GraphRAG retrieves entities and their relationships rather than documents. Excels at causal reasoning, multi-hop queries, and structured knowledge domains.
How to Choose
Begin with Standard RAG and master the fundamentals. Add Conversational memory only if users need multi-turn interactions. Match your architecture to your actual query patterns: Adaptive for variable complexity, Corrective for high accuracy requirements, Fusion for ambiguous or underspecified queries, and GraphRAG for relational data. In practice, production systems blend two or more patterns. The best system is not the most sophisticated — it is the one that reliably serves your users within your constraints.
Sources
Lewis et al. — Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (2020)
Gao et al. — Retrieval-Augmented Generation for Large Language Models: A Survey (2024)