Transforming Information Retrieval with AI
This resource delves into Retrieval-Augmented Generation (RAG), a cutting-edge AI approach that boosts Large Language Models (LLMs). RAG empowers LLMs with external data, moving beyond fixed training sets. It fetches pertinent information on demand for improved accuracy, reliability, and contextual understanding. Learn the core RAG process, examine different architectures, and analyze their strengths and weaknesses to master creating dependable RAG solutions.
The Foundational RAG Pipeline
> The RAG workflow has two primary phases: offline ingestion (knowledge preparation) and online inference (query answering). Click each to discover its role and applicable patterns.
Phase 1: Ingestion (Offline)
Phase 2: Inference (Online)
A Deep Dive into RAG Patterns
RAG patterns span basic to advanced agentic frameworks, boosting retrieval quality. Use filters to explore patterns by their pipeline function.
Choose a pattern below or from the comparison chart to learn more.
Comparative Analysis of RAG Patterns
* **See RAG patterns side-by-side.** This chart uses interactive bubbles, each representing a pattern. Their position reflects function and complexity. Click any bubble for in-depth information. * **Quickly compare RAG patterns with this interactive chart.** Patterns are visualized as bubbles, placed according to their role and complexity. Click a bubble to reveal detailed information above.