From Retrieval to Reasoning
An interactive journey through the evolution of Retrieval-Augmented Generation (RAG), from simple pipelines to autonomous, reasoning agents.
The Foundations: An Interactive Look at Naive RAG
Click on each step to see how data flows through the foundational RAG pipeline.
1. Indexing
Documents are chunked, embedded, and stored in a vector database.
2. Retrieval
User query is embedded to find the most similar chunks via vector search.
3. Generation
The query and retrieved context are fed to an LLM to produce a grounded answer.
Select a step above to see more details.
RAG Paradigms Compared
See how RAG has evolved from a simple pipeline to complex, reasoning systems.
Philosophy: Simple & Linear
A straightforward, retrieve-then-generate process. Easy to implement and a good baseline, but brittle and prone to retrieval errors.
Philosophy: Modular Optimization
Optimizes each step (pre-retrieval, retrieval, post-retrieval) with techniques like query transformation, hybrid search, and reranking for higher accuracy.
Philosophy: Dynamic Reasoning
Uses an LLM agent to plan, use tools (like multiple retrievers or APIs), and reason over information to solve complex, multi-step queries autonomously.
The Frontier: State-of-the-Art Architectures
Modern systems that embed reasoning, self-correction, and autonomous decision-making into the RAG process. Flip cards to learn more.
Self-RAG
Adaptive retrieval and generation through self-reflection.
Self-RAG
An LLM learns to generate special "reflection tokens" to decide when to retrieve information, assess its relevance, and verify if its own output is factually grounded.
Corrective RAG
Robustness through automated correction of retrieval failures.
Corrective RAG (CRAG)
Uses a lightweight "retrieval evaluator" to score retrieved documents. If quality is low, it triggers a web search to find better information.
Agentic RAG
Autonomous, reasoning-driven orchestration of tasks.
Agentic RAG
An LLM acts as an "agent" that can reason, plan, and use a diverse set of tools to solve complex, multi-step queries.
GraphRAG
Leveraging interconnected knowledge via knowledge graphs.
GraphRAG
Retrieves from a Knowledge Graph, allowing it to answer multi-hop questions by traversing entity relationships across documents.
Multimodal RAG
Extending RAG beyond text to images, audio, and video.
Multimodal RAG
Integrates non-textual data using multimodal embeddings or by generating textual descriptions of visual content for grounding.
Future Trajectories
The research directions shaping the next generation of knowledge-grounded AI.
Deeper Reasoning & Planning
Systems will feature more sophisticated planning capabilities to tackle complex problems that require long-term, adaptive strategies.
Self-Improving Systems
Using reinforcement learning and user feedback to continuously optimize retrieval and generation strategies, allowing systems to improve over time.
Pervasive Multimodality
Seamlessly reasoning over text, images, audio, and video will become a standard expectation, unlocking new applications.
Real-Time & Federated RAG
Integration with real-time data streams and decentralized, on-device knowledge bases to ensure currency and enhance privacy.