Awesome Retrieval‑Augmented Generation (RAG)

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Retrieval‑Augmented Generation (RAG) equips language models with fresh, domain‑specific knowledge by fetching external context at inference time. This list is a one‑stop catalogue of every major RAG‑related resource—tools, papers, benchmarks, tutorials, and more.

Only very short descriptions are provided when essential for clarity. PRs welcome!

Table of Contents

• Open Source Tools
• Embedding Models & Libraries
• Proprietary Tools
• Vendor Examples
• Vector DBs & Search Engines
• Research Papers and Surveys
• RAG Approaches and Architectures
• Frameworks
• RAG Techniques and Methodologies
  • Multimodal RAG
  • Graph-based RAG
• Retrieval Methods
  • Dense Retrieval
  • Sparse Retrieval
  • Hybrid Search
  • Other Techniques
• Prompting Strategies
• Chunking & Pre‑processing
• Embeddings Models
• Instruction Tuning & Optimization
• Finetuning and Training
• Response Quality, and Hallucination
• Security and Privacy Considerations
• Evaluation Metrics and Benchmarks
• Advantages and Disadvantages
• Performance, Cost & Observability
  • Cost Calculators
• RAG Fine-tuning
• Knowledge‑Graph / Structured RAG
• Libraries and SDKs
• Key Concepts
• Educational Content
  • Courses and Tutorials
  • Blogs and Articles
  • Newsletters & Forums
  • Talks and Conferences
• Influential Researchers and Influencers
• Latest Trends 2024-2025
• Community Resources

Open Source Tools

• CustomGPT.ai - Open-source SDK for building custom RAG applications with enterprise-grade features
• TrustGraph - Open-source enterprise-grade complete AI solution stack for data sovereignty
• RAGFlow - Open-source RAG engine based on deep document understanding
• R2R (RAG to Riches) - Advanced AI retrieval system with production-ready features
• FastRAG - Research framework for efficient retrieval augmented generation
• FlashRAG - Python toolkit for RAG research with 36+ datasets and 17+ algorithms
• Verba - Open-source RAG application out of the box
• Kotaemon - Clean, customizable RAG UI for document-based Q&A
• Cognita - Open-source RAG framework for modular applications
• GraphRAG - Microsoft's approach to RAG using knowledge graphs
• Nano-GraphRAG - Compact GraphRAG solution with core capabilities

• LangChain — Python/JS agents & chains
• LangChain4j — JVM
• LlamaIndex — Data loaders & indices
• Haystack — Modular pipelines
• Semantic Kernel — .NET & Python
• DSPy — Declarative pipelines
• Guidance — Prompt DSL
• Flowise — No‑code builder
• reag — Reasoning Augmented Generation
• Danswer — Internal Q&A search
• Neum — Creation and synchronization of vector embeddings at large scale
• GPTCache — Embedding‑aware cache
• Mastra The TypeScript AI agent framework. Assistants, RAG, observability. Supports any LLM: GPT-4, Claude, Gemini, Llama
• Letta (MemGPT) — Stateful apps
• Swiftide - Fast, streaming indexing, query, and agentic LLM applications in Rust
• LangGraph — Agentic DAGs
• Ragna — RAG orchestration framework
• SimplyRetrieve - Lightweight chat AI platform featuring custom knowledge.

Embedding Models & Libraries

• OpenAI text‑embedding‑3
• Cohere Embed v3
• FlagEmbedding
• Jina Embeddings v4
• E5/GTE (MTEB)
• SentenceTransformers
• MiniLM
• ColBERT v2
• Voyage AI Embeddings
• BGE family
• Nomic Embed Text
• fastText — char n‑gram baseline

Proprietary Tools

• CustomGPT.ai RAG API — Enterprise agents, hallucination free.
• Pinecone - Fully managed vector database service
• LangSmith - Platform for building and evaluating LLM applications
• OpenAI Assistants & Retrieval
• Vectara — GenAI API
• Cohere RAG
• AWS Knowledge Bases for Bedrock
• Azure AI Search + RAG
• Google Vertex AI Search & RAG
• IBM watsonx.ai Retrieval
• NVIDIA NeMo Retriever
• Anthropic Claude Retrieval
• Databricks DBRX RAG
• Elastic Search Labs RAG blueprints

Vendor Examples

• Amazon Kendra - Intelligent enterprise search with RAG
• Amazon Bedrock Knowledge Bases
• Azure AI Search
• Google Vertex AI Search
• LangChain × OpenAI Quickstart
• LangChain × Elasticsearch Blueprint
• LlamaIndex × Vespa Guide
• Qdrant Hybrid Search miniCOIL
• AWS Bedrock RAG Sample
• Azure RAG Jumpstart
• GCP Vertex RAG Agent Builder

Other Tools

• LangFuse: Open-source tool for tracking LLM metrics, observability, and prompt management.
• Ragas: Framework that helps evaluate RAG pipelines.
• LangSmith: A platform for building production-grade LLM applications, allows you to closely monitor and evaluate your application.
• Hugging Face Evaluate: Tool for computing metrics like BLEU and ROUGE to assess text quality.
• Weights & Biases: Tracks experiments, logs metrics, and visualizes performance.

Vector DBs & Search Engines

Pick a vector db - GUIDE

• Weaviate - Open-source vector database with GraphQL interface
• Qdrant - High-performance vector similarity search engine
• Milvus - Open-source vector database for scalable similarity search
• Chroma - Open-source embedding database for LLM applications
• Pinecone - The vector database
• Elasticsearch (vector) - distributed search and analytics engine
• OpenSearch - Open source distributed and RESTful search engine
• Vespa - AI + Data, online
• PGVector - PostgreSQL extension for vector similarity search
• Redis Stack Search - Searching and querying Redis data using the Redis Query Engine
• ClickHouse Vectors
• Oracle AI Vector Search
• TiDB Vector - semantic similarity searches across various data types
• ScaNN - ScaNN (Scalable Nearest Neighbors) is a method for efficient vector similarity search at scale
• Lantern.dev - open-source Postgres vector database
• Azure Cosmos DB: Globally distributed, multi-model database service with integrated vector search.
• Couchbase: A distributed NoSQL cloud database.
• LlamaIndex: Employs a straightforward in-memory vector store for rapid experimentation.
• Neo4j: Graph database management system.
• Redis Stack: An in-memory data structure store used as a database, cache, and message broker.
• SurrealDB: A scalable multi-model database optimized for time-series data.

Research Papers and Surveys

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks - Original RAG paper by Patrick Lewis et al.
• REALM: Retrieval-Augmented Language Model Pre-Training - Google's foundational retrieval-augmented language model
• Dense Passage Retrieval for Open-Domain Question Answering - Facebook's DPR system for dense retrieval
• Retrieval-Augmented Generation for Large Language Models: A Survey - Comprehensive survey covering Naive RAG, Advanced RAG, and Modular RAG
• A Comprehensive Survey of Retrieval-Augmented Generation (RAG) - 2024 survey tracing RAG evolution from foundational concepts to current state
• Retrieval-Augmented Generation for AI-Generated Content: A Survey - Comprehensive review of RAG techniques for AIGC scenarios
• Evaluation of Retrieval-Augmented Generation: A Survey - Comprehensive overview of RAG evaluation methodologies
• (2020)Retrieval‑Augmented Generation for Knowledge‑Intensive NLP Tasks - Lewis et al. — RAG baseline
• (2020) REALM - Guu et al. — Retriever‑augmented pre‑training
• (2022) Atlas - Izacard & Grave — Few‑shot RAG
• (2022) RETRO - Borgeaud et al. — Large‑scale retrieval cache
• (2024) Benchmarking LLMs in RAG - Chen et al.
• (2024) Reliable, Adaptable & Attributable LMs with Retrieval - Dan et al.
• (2024) GraphRAG - Microsoft Research
• (2024) RAG‑Fusion - Meta
• (2025) Look‑ahead Retrieval - OpenAI

More - RAG Research Papers Collection - Curated list from ICML, ICLR, ACL

RAG Survey 2022

• A Survey on Retrieval-Augmented Text Generation

RAG Survey 2023

• Retrieving Multimodal Information for Augmented Generation: A Survey
• Retrieval-Augmented Generation for Large Language Models: A Survey

RAG Survey 2024

• Retrieval-Augmented Generation for AI-Generated Content: A Survey
• A Survey on Retrieval-Augmented Text Generation for Large Language Models
• RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
• Evaluation of Retrieval-Augmented Generation: A Survey
• Retrieval-Augmented Generation for Natural Language Processing: A Survey
• Graph Retrieval-Augmented Generation: A Survey
• Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely
• A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions

RAG Approaches and Architectures

• Fusion-in-Decoder (FiD)
• RETRO (Retrieval-Enhanced Transformer) - DeepMind's approach with trillions of tokens
• Atlas: Few-shot Learning with Retrieval Augmented Language Models - Meta's Atlas model for few-shot learning
• ColBERT: Efficient Late Interaction Retrieval - Multi-vector dense retrieval with late interaction
• Cache-Augmented Generation (CAG) – Pre-loads pertinent documents into the model’s context and retains the key-value (KV) cache from earlier inferences.
• Agentic RAG – “Retrieval agents” that autonomously decide how and when to retrieve information.
• Corrective RAG (CRAG) – Adds a refinement step to fix or polish retrieved content before it is woven into the LLM’s answer.
• Retrieval-Augmented Fine-Tuning (RAFT) – Fine-tunes language models specifically to boost both retrieval quality and generation performance.
• Self-Reflective RAG – Systems that monitor their own outputs and dynamically adjust retrieval strategies based on feedback.
• RAG Fusion – Blends multiple retrieval techniques to supply richer, more relevant context.
• Temporal Augmented Retrieval (TAR) – Incorporates time-aware signals so retrieval favors the most temporally relevant data.
• Plan-then-RAG (PlanRAG) – Creates a high-level plan first, then executes retrieval-augmented generation for complex tasks.
• GraphRAG – Leverages knowledge graphs to structure context and enhance reasoning.
• FLARE – Uses active, iterative retrieval to progressively improve answer quality.
• Contextual Retrieval – Enriches document chunks with added context before retrieval, improving relevance from large knowledge bases.
• GNN-RAG – Applies graph neural networks to retrieval for better reasoning in large-language-model workflows.

Frameworks

• LangChain - Framework for building LLM applications with chaining capabilities
• LlamaIndex - Framework for connecting custom data sources to LLMs
• Haystack - End-to-end framework for building production-ready LLM applications
• DSPy - Framework for programming language models with automatic optimization
• Dify - Open-source LLM app development platform with RAG pipeline
• Semantic Kernel - Microsoft's SDK for developing Generative AI applications
• Flowise - Drag & drop UI to build customized LLM flows
• Cognita: Open-source RAG framework for building modular and production ready applications.
• Verba: Open-source application for RAG out of the box.
• Mastra: Typescript framework for building AI applications.
• Letta: Open source framework for building stateful LLM applications.
• Swiftide: Rust framework for building modular, streaming LLM applications.
• CocoIndex: ETL framework to index data for AI, such as RAG; with realtime incremental updates.

RAG Techniques and Methodologies

• HyDE (Hypothetical Document Embeddings) - Uses LLMs to generate hypothetical documents for queries
• FLARE (Forward-Looking Active REtrieval) - Iteratively retrieves relevant documents based on prediction confidence
• Self-RAG - Trains LLMs to adaptively retrieve passages and self-critique
• CRAG (Corrective Retrieval Augmented Generation) - Improves generation robustness with retrieval evaluator
• RAG Techniques Repository - Curated collection of 30+ advanced RAG techniques with implementations
• Design and Evaluation of RAG Solutions - Comprehensive guide following best practices
• LangChain RAG Best Practices - Evaluation and comparison of different RAG architectures
• RAG Triad Methodology - Context relevance, groundedness, and answer relevance framework
• Agentic RAG
• Corrective RAG (CRAG)
• Cache‑Augmented Generation
• Temporal‑Aware RAG - Binary duadic codes and their related codes with a square-root-like lower bound
• Plan‑then‑RAG - A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers
• RePlug — Retriever‑aware generation
• RETRO — Retro‑fitted retrieval
• Streaming RAG — Low latency

Multimodal RAG

• Multimodal RAG with CLIP - Text-Image retrieval using CLIP
• SAM-RAG - Self-adaptive multimodal RAG framework
• ColPali - Efficient document retrieval with vision language models
• Building Multimodal RAG Systems

Graph-based RAG

• Microsoft GraphRAG - Knowledge graph approach to RAG Research: GraphRAG Paper
• Knowledge Graph Integration for RAG
• Neo4j GraphRAG - Building knowledge graphs for RAG

Retrieval Methods

Dense Retrieval

• Dense Passage Retrieval (DPR) Implementation
• ColBERTv2: Effective and Efficient Retrieval

Sparse Retrieval

• SPLADE: Sparse Lexical and Expansion Model - Neural sparse retrieval with term expansion
• HNSW vs DiskANN

Hybrid Search

• Hybrid Search: Combining Dense and Sparse Retrieval - Implementation guide for hybrid search systems
• Dense‑Sparse‑Dense (DSD)
• Advanced Reranking Techniques - Guide to implementing cross-encoder reranking.

More here: All RAG Reranking (GitHub)

Other Techniques

• RAG Fusion
• Sentence Window Retrieval
• Cross‑Encoder Re‑Ranking
• Gemini Small‑to‑Big Retriever
• Multi‑Vector Retrieval
• Negative PRF (Pseudo‑Relevance)

Prompting Strategies

• RAG Prompt Engineering Guide (DAIR.AI) - Comprehensive guide to prompt engineering for RAG systems

• LangChain RAG Prompt Hub - Collection of tested RAG prompt templates

• Efficient Prompt Engineering for RAG - Strategies for optimizing prompts in RAG systems

• Secure RAG applications using prompt engineering on Amazon Bedrock - Best practices for RAG prompts with security considerations

• Zero‑Shot / Few‑Shot
• Chain‑of‑Thought (CoT)
• Meta Prompting
• Generated Knowledge Prompting
• ReAct
• Reflexion
• Automatic Prompt Engineer (APE)
• Directional Stimulus Prompting (DSP)
• Chain‑of‑Verification (CoVe)
• Self‑Consistency
• Prompt Compression
• Dynamic / Adaptive Prompts
• System → Retrieval → User triple‑prompt
• GraphPrompt
• Emerging RAG & Prompt Engineering Architectures for LLMs
• How to Cut RAG Costs by 80% Using Prompt Compression

Chunking & Pre‑processing

• 11 Chunking Strategies for RAG — Simplified & Visualized - Comprehensive guide covering 11 chunking methods with visual comparisons
• 5 Levels of Text Splitting - Hierarchical approach to chunking from basic to advanced
• Semantic Chunking with LlamaIndex - Implementation guide for semantic-based document splitting
• Optimizing Retrieval-Augmented Generation with Advanced Chunking Techniques - Research on optimal chunk sizes for different use cases
• CharacterTextSplitter — fixed‑size
• RecursiveTextSplitter
• SentenceSplitter (LlamaIndex)
• Unstructured‑IO loaders
• LoRA Chunking - Fused kernel chunk loss to include LoRA to reduce memory, support DeepSpeed ZeRO3
• Semantic chunking video
• Agentic chunking demo - The 5 Levels Of Text Splitting For Retrieval
• Chunking Strategies for LLM Applications
• Evaluating the Ideal Chunk Size for a RAG System using LlamaIndex
• How to Chunk Text Data — A Comparative Analysis

Comparison Guides

• Vector Database Comparison: Pinecone vs Weaviate vs Chroma - Comprehensive enterprise-focused comparison with performance metrics

• Top Vector Database for RAG: Qdrant vs Weaviate vs Pinecone - Performance comparison of 6 vector databases for RAG workloads

Embeddings Models

• Embedding Model Comparison: OpenAI vs Cohere vs Open Source - Comprehensive evaluation of commercial and open-source embedding models

• Best Embedding Model — OpenAI / Cohere / Google / E5 / BGE - Detailed comparison of top embedding models with performance metrics

• Matryoshka Embeddings for RAG - Implementing variable-size embeddings for efficiency

• BGE M3 and SPLADE Implementation Guide - Guide to implementing sparse and dense embeddings

Instruction Tuning & Optimization

• RA‑DIT
• InstructRetro
• FLARE / Active RAG
• UltraFeedback — RLHF on RAG
• DSI‑T — Decoder‑only retrieval

Finetuning and Training

• Fine-Tuning Llama 2.0 with Single GPU Magic
• Practitioners guide to fine-tune LLMs for domain-specific use case
• Are You Pre-training your RAG Models on Your Raw Text?
• Combine Multiple LoRA Adapters for Llama 2
• RAG vs Finetuning — Which Is the Best Tool to Boost Your LLM Application?

Response Quality, and Hallucination

• RAGTruth: A Hallucination Corpus - Dataset with 18,000 RAG responses and hallucination annotations
• Reducing Hallucination in Structured Outputs via RAG
• WhyLabs AI Control Center - Platform for real-time guardrails and monitoring
• Vectara Hallucination Score
• Prompt‑Injection Defense
• OpenAI Function Calling JSON Schema
• Harmless RLHF pipelines
• in-Of-Verification Reduces Hallucination in LLMs
• How to Detect Hallucinations in LLMs
• Measuring Hallucinations in RAG Systems

Security and Privacy Considerations

• OWASP Top 10 for LLM Applications - Comprehensive security framework covering RAG vulnerabilities
• CSA RAG Security Best Practices - Enterprise-grade security controls for RAG
• Microsoft Presidio for PII Protection - Framework for detecting and anonymizing sensitive information
• LLM Guard - Security toolkit for protecting LLM applications
• Masking PII Data in RAG Pipeline
• Hijacking Chatbots: Dangerous Methods Manipulating GPTs
• Guardrails AI - Framework for implementing security guardrails
• NVIDIA NeMo Guardrails - Comprehensive toolkit for building programmable guardrails
• NeMo Guardrails: The Missing Manual
• Safeguarding LLMs with Guardrails

Evaluation Metrics and Benchmarks

• RAGAS (Retrieval-Augmented Generation Assessment) - Reference-free evaluation framework with component-level metrics
• TruLens - Comprehensive evaluation and tracking for LLM applications
• DeepEval - Open-source evaluation framework for LLMs
• Arize Phoenix - Open-source observability platform
• RAGBench - 100k examples across 5 industry domains
• BeIR - Benchmark for zero-shot evaluation of information retrieval
• MTEB - Massive Text Embedding Benchmark
• ARES - Automated Evaluation of RAG Systems
• RGB Benchmark - implementation for Benchmarking Large Language Models in Retrieval-Augmented Generation
• LlamaIndex RAG eval - Evaluation and benchmarking are crucial in developing LLM applications

Blogs

• RAG Evaluation
• Evaluating RAG: A journey through metrics
• Exploring End-to-End Evaluation of RAG Pipelines
• Evaluation Driven Development, the Swiss Army Knife for RAG Pipelines
• Evaluating the Ideal Chunk Size for a RAG System using LlamaIndex

RAG Benchmark 2023

• Benchmarking Large Language Models in Retrieval-Augmented Generation
• RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
• ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
• RAGAS: Automated Evaluation of Retrieval Augmented Generation

RAG Benchmark 2024

• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
• FeB4RAG: Evaluating Federated Search in the Context of Retrieval Augmented Generation
• CodeRAG-Bench: Can Retrieval Augment Code Generation?
• Long²RAG: Evaluating Long-Context & Long-Form Retrieval-Augmented Generation with Key Point Recall

Advantages and Disadvantages

• Advantages overview
• Disadvantages & pitfalls
• RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study

Performance, Cost & Observability

• Vector Database Optimization - Techniques for efficient vector storage and retrieval
• Hybrid Retrieval Strategies - Combining multiple retrieval methods for better performance
• Chunking Optimization - Strategies for optimal text segmentation
• LangFuse
• LangSmith
• Helicone — telemetry
• WandB RAG guide
• OpenLLMetry - Open-source observability for your LLM application, based on OpenTelemetry
• Cost optimisation tips

Cost Calculators

• RAG Cost Calculator - Tool for estimating and optimizing RAG pipeline costs
• RAG Savings Calculator
• RAG Cost Calculator

RAG Fine-tuning

• RAFT (Retrieval Augmented Fine-Tuning) - Adapting Language Model to Domain Specific RAG
• Fine-tuning vs RAG Guide - Comprehensive comparison and guidance
• Direct Preference Optimization (DPO) for RAG - Alternative to RLHF for aligning RAG outputs

Knowledge‑Graph / Structured RAG

• DBpedia - Structured knowledge from Wikipedia
• Wikidata - Community-maintained knowledge base
• ConceptNet - Large-scale commonsense knowledge graph
• YAGO - High-quality knowledge base
• Neo4j LLM Knowledge Graph Builder
• Neo4j RAG blog
• GraphRAG site
• NebulaGraph Graph‑RAG article

Libraries and SDKs

• Sentence Transformers - Python framework for sentence, text and image embeddings
• LiteLLM - Python SDK for 100+ LLM APIs in OpenAI format
• AI SDK - TypeScript toolkit for building AI applications
• Hugging Face Transformers - State-of-the-art ML for PyTorch, TensorFlow, and JAX

Key Concepts

• Hugging Face Transformers - RAG Documentation
• RAG-Survey GitHub Repository - Curated collection of RAG papers with taxonomy

Educational Content

Courses and Tutorials

• Building Advanced RAG (DeepLearning.AI)
• RAG from Scratch (FreeCodeCamp)
• IBM Generative AI and RAG Course (Coursera)
• MAGMaR 2024 — Multimodal Augmented Generation (NeurIPS)
• ACL 2024 Knowledgeable LMs Tutorial
• SIGIR 2023 Generative IR Workshop

• MAGMaR - The 1st Workshop on Multimodal Augmented Generation via MultimodAl Retrieval - Reno Kriz, Kenton Murray, Eugene Yang, Francis Ferraro, Kate Sanders, Cameron Carpenter, Benjamin Van Durme

• Towards Knowledgeable Language Models - Zoey Sha Li, Manling Li, Michael JQ Zhang, Eunsol Choi, Mor Geva, Peter Hase

• Modular RAG and RAG Flow Yunfan Gao (2024) Tutorial - Blog I and Blog II

• Stanford CS25: V3 I Retrieval Augmented Language Models Douwe Kiela (2023) Lecture - Video

• Building RAG-based LLM Applications for Production Anyscale (2023) Tutorial - Blog

• Multi-Vector Retriever for RAG on tables, text, and images LangChain (2023) Tutorial - Blog

• Retrieval-based Language Models and Applications Asai et al. (2023) Tutorial ACL Website and Video

• Advanced RAG Techniques: an Illustrated Overview Ivan Ilin (2023) Tutorial - Blog

• Retrieval Augmented Language Modeling Melissa Dell (2023) Lecture Video

• RAG from scratch
• Building RAG Applications for Production
• Stanford CS25: V3 I Retrieval Augmented Language Models
• Ray Summit 2024: Production RAG Pipelines
• Haystack RAG Workshop
• Azure Cognitive Search RAG Tutorial

Blogs and Articles

• RAG Implementation with LangChain and Weaviate - From theory to Python implementation
• Advanced RAG Techniques: An Illustrated Overview
• The RAGOps Stack: Critical Components
• Knowledge Graphs for RAG
• RAG Intuitively & Exhaustively Explained
• RAG in Production: 9 Lessons
• Reranking vs Embeddings on Cursor
• Forget RAG, Think RAG‑Fusion
• Hidden Costs of RAG

Newsletters & Forums

• ragaboutit - A blog and newsletter focused specifically on RAG news, tutorials, and insights, making it a dedicated resource for staying up-to-date.
• r/LangChain
• r/rag - Reddit communities for practical discussions, troubleshooting, and sharing projects. These are valuable for seeing what challenges other developers are facing in real-time.

Talks and Conferences

• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (NeurIPS 2020)
• Self-RAG: Learning to Retrieve, Generate, and Critique (ICLR 2024)
• RAG Research Papers Collection - Curated list from ICML, ICLR, ACL

Influential Researchers and Influencers

• Patrick Lewis - Lead author of original RAG paper, AI Research Scientist at Cohere
• Sebastian Riedel - Co-author of RAG paper, Professor at UCL and DeepMind
• Douwe Kiela - Co-author of RAG paper, CEO of Contextual AI
• Gautier Izacard - Author of FiD and Atlas papers, Meta AI
• Kelvin Guu - Lead author of REALM paper, Google Research
• Douwe Kiela — Modular RAG, Stanford
• Matei Zaharia — DSPy, Databricks
• Akari Asai — Dense retrieval research
• Jerry Liu — LlamaIndex
• Harrison Chase — LangChain
• Andrej Karpathy — LLM systems
• Jeff Dean — Google Research
• Artem Yankov — Qdrant
• Alden Do Rosario - RAG Influencer, CEO CustomGPT.ai

Latest Trends 2024-2025

• RAG-as-a-Service market at $1.2B (2024)
• Projected 49.1% CAGR through 2030
• On-device RAG for privacy

Community Resources

Reddit

• r/LocalLLaMA - 493k members
• r/MachineLearning - Active RAG discussions
• r/RAG - Dedicated RAG subreddit

Discord

• RAG TAGG Discord - 2,492 members
• Vectara RAGTime Bot
• RAGHub Community

GitHub Communities

• RAGHub Repository
• Microsoft GraphRAG

Existing Collections

• Awesome-RAG (awesome-rag)
• RAG Resources (mrdbourke)
• RAG Techniques (NirDiamant)
• Danielskry / Awesome‑RAG
• frutik / Awesome‑RAG
• coree / awesome‑rag
• jxzhangjhu / Awesome‑LLM‑RAG
• SJTU‑DMTai / awesome‑rag
• lucifertrj / Awesome‑RAG

Contributing

Contributions are welcome! Please read the contribution guidelines before submitting a pull request.

License

This collection is licensed under MIT.