Introduction

Retrieval-Augmented Generation (RAG) represents a powerful paradigm in AI that bridges the gap between static language models and dynamic knowledge systems. Rather than a single technique, RAG encompasses a family of methods that share a common philosophy: enhancing language model outputs by incorporating external knowledge retrieved at inference time.

This hybrid architecture creates a flexible framework that can be adapted to various applications and domains. The RAG approach has spawned numerous implementations and variations, each optimizing different aspects of the retrieval-generation pipeline.

🟦 Auto-RAG

🟦 Corrective RAG

🟦 FLARE / Active RAG

🟦 GraphRAG

🟦 HybridRAG

🟦 InFO-RAG

🟦 Multi-Fusion Retrieval Augmented Generation (MoRAG)

🟦 R^2AG

🟦 Retrieval-Augmented Generation (RAG)

🟦 Reliability-Aware RAG (RA-RAG)

🟦 Self-RAG

🟦 Speculative RAG

Footnotes

1. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., tau Wen-Yih, Rocktäschel, T., Riedel, S., & Kiela, D. (2021). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. https://arxiv.org/abs/2005.11401 ↩