Selected architecture
Vanilla RAG
Process flow
Primary strength
What it does well
Key limitation
Retrieval-Augmented Generation
The Evolution of RAG
A Web Guide: RAG Architectures, Concepts, and Agents. Explore how retrieval patterns evolve from a simple retrieve-then-read pipeline into hybrid, cache-augmented, hypothetical-document, and agent-directed workflows.
Architecture explorer
Choose a RAG pattern and inspect how it works
The source page describes six related approaches. Select one to compare its process flow, primary strength and key limitation without leaving the page.
Comparative analysis
Compare RAG architectures with ease
See how different Retrieval-Augmented Generation paradigms stack up. The original page used a radar chart; this rebuild presents the same relative 1–5 profile in a more accessible table.
Interpret these scores as design guidance, not benchmarks
The 1–5 values are qualitative relative ratings from the supplied page. They are useful for understanding intended trade-offs, but they are not empirical performance measurements and should not be treated as benchmark results.
| Architecture | Complexity | Flexibility | Speed | Accuracy | Scalability |
|---|
Detailed comparison
Six RAG patterns, side by side
Use the matrix to compare each pattern’s core concept, common use case, principal strength, main limitation and relative implementation complexity.
Architecture takeaway
There is no universally best RAG architecture.
The right pattern depends on the knowledge base, update frequency, latency target, query ambiguity, retrieval quality, operational complexity and how much dynamic reasoning the workflow actually needs.
A practical progression
- 1Start with the simplest pattern that can satisfy the use case.
- 2Add query processing, reranking or hybrid retrieval when evaluation exposes retrieval failures.
- 3Use agentic retrieval when the task genuinely requires dynamic planning, tool selection or iterative research.