Retrieval-Augmented Generation (RAG) enhances AI responses with relevant information from your document collections and knowledge bases.
Features
- 📄Document Upload - Upload and manage documents for AI retrieval
- 🔢Vector Embeddings - Automatic creation of vector embeddings for semantic search
- 🎯Retrieval Strategies - Configure how information is retrieved
- 📁Collections - Organize documents into knowledge collections
How RAG Works
- Upload Documents - Add your PDFs, text files, or other documents
- Processing - Documents are chunked and embedded into vectors
- Storage - Vectors are stored in the knowledge base
- Query - When you ask a question, relevant chunks are retrieved
- Generation - AI generates a response using the retrieved context
💡 Pro Tip
Organize documents into collections by topic for more accurate retrieval. Use descriptive names for your collections.
RAG Examples & Use Cases
A comprehensive list of RAG (Retrieval-Augmented Generation) implementations organized by category.
🏢 Enterprise & Business Applications
Customer Support Chatbots
- 🚚DoorDash – Delivery support chatbot for "Dashers" with LLM guardrails and judge systems for quality control
- 💼LinkedIn – Customer service QA combining RAG with knowledge graphs, reducing resolution time by 28.6%
- 🏦JPMorgan Chase (EVEE) – Intelligent Q&A for call center specialists across fraud, lending, and wealth management
- 📰Thomson Reuters – Executive customer support with curated database access
- 🔔Bell – Internal policies chatbot with modular document embedding pipelines
Enterprise Search & Knowledge Management
- 📈Bloomberg – Financial transcript and earnings report summarization with source verification
- 🏦Royal Bank of Canada (Arcane) – Internal policy locator for banking specialists
- 🎓Harvard Business School (ChatLTV) – AI professor chatbot for entrepreneurship courses trained on case studies
Sales & Marketing
- 🔭Telescope – Personalized lead recommendations using CRM data
- 📝RFP Automation – Automated response generation for sales proposals pulling from product docs
- 🚗Sales meeting analysis – Virtual ride-along software providing feedback on client appointments
🏥 Healthcare & Scientific Applications
- 🧬BioRAGent – Interactive biomedical QA system linked to PubMed sources
- 📄PaperQA – Research paper analysis achieving 86.3% accuracy on PubMedQA (vs 57.9% for GPT-4 alone)
- 🏥IBM Watson Health – Oncology treatment recommendation matching expert oncologists 96% of the time
- 💊CLADD – Drug discovery framework connecting LLMs to biochemical databases and knowledge graphs
💼 Financial & Compliance Services
Fraud Detection & Risk Assessment
- 🎓University of Waterloo – Phone scam detection with 98% accuracy using real-time transcription
- 💳Mastercard – Voice scam detection with 300% increase in detection rates
- 🚕Grab (A* bot) – Fraud investigation assistant using Data-Arks APIs and RAG-powered queries
Compliance & Audit
- 📊Ramp – Industry classification migration to NAICS standards using RAG
- 📋Compliance assistants – Automated cross-referencing of internal docs with GDPR, HIPAA, ISO regulations
🔧 Technical & Developer Tools
Code & Data Analysis
- 📌Pinterest – Text-to-SQL assistant with RAG-guided table selection for data analysts
- 📊Grab (Report Summarizer) – Automated analytical report generation saving 3-4 hours per report
- 💻Code search RAG – AST-aware chunking strategies for code repositories
IT Support & Operations
- 🛠️IT troubleshooting bots – Analyzing internal documentation, ticket history, and system logs
- ⚡Pattern recognition – Suggesting fixes before issues escalate
🎓 Education & HR
- 👥HR assistants – Answering policy questions from handbooks and onboarding materials
- 🎯Interview preparation agents – Pulling data from applicant tracking systems
🛠️ Open-Source Framework Examples
- 🧱Kotaemon – Modular LEGO-style Q&A toolkit with customizable RAG UI
- ⚡FlashRAG – Research toolkit with 12 state-of-the-art RAG implementations and 32 benchmark datasets
- 🔗Camel-GraphRAG – Hybrid vector + knowledge graph retrieval using Mistral and Neo4j
- 🌾Haystack – Enterprise pipeline-centric framework with composable nodes
- 🌐Cognita – Production-ready scalable RAG with web UI for non-developers
- 🤖Agentic RAG – Autonomous information retrieval systems using LLM-driven agents
Screenshots
RAG Collections