Overview:
Retrieval Augmented Generation (RAG) is favored in enterprise chatbots for its ability to integrate company-specific data, enhancing AI grounding. This approach has transformative potential across various sectors like customer support, business intelligence, and research, offering a competitive edge. RAG’s impact is particularly notable in e-commerce, finance, law, and education, driving innovation and operational efficiency. However, deploying RAG requires careful navigation of privacy concerns, ensuring compliance with regulations while maintaining user trust and personalized experiences for enhanced customer interaction. The report explores RAG’s application in enterprises, highlighting its significance and challenges.
Table of Contents:
- Quick Overview of RAG
- Section 1: Background
- Section 2: The Shortcomings of Off-the-shelf LLMs
- Section 3: Three Key Approaches for Mitigation
- Fine-tuning
- Prompt Engineering
- Retrieval Augmented Generation (RAG)
- Comparative Analysis
- Section 4: RAG Architecture Diagram
- Section 5: Major RAG Technology Providers
- NVIDIA: Accelerating RAG Inference with “NeMo Retriever”
- Google: Leveraging its Google Cloud Database
- ChatGPT: Two New Embedding Models for RAG
- Pinecone: High-performance Vector Search for Data Retrieval
- AI21labs: RAG Engine as “All-in-one” Solution
- Microsoft: Multiple Advancements of RAG
- Amazon: RAG-enabled Amazon Bedrock in AWS
- Section 6: Top RAG Use Cases in Enterprises
- Case Study 1: Generali’s Q&A Chatbot, Customer Support, Insurance Industry
- Case Study 2: Shopify’s Conversational Agent for External Merchant, Business Intelligence, E-commerce Industry
- Case Study 3: Casetext’s Content Creation System, Research Studies, Legal Industry
- Section 7: Current Challenges and Limitations
- Section 8: Conclusion
Number of Pages: 19
Publication Date: March 2024
