Connect GPT with your data: Retrieval-augmented Generation
summary
Shows how to connect LLMs to private company data using retrieval-augmented generation with concrete code in Haystack, a live demo that swaps plain GPT answers for context-grounded ones, and a pipeline built from a retriever + prompt node using vector database embeddings. Claims retrieval cuts hallucinations, adds explainable citations, and supports private VPC deployments; notes >50 enterprise customers tested this, gives a cost example (100k-token call ≈ $110), and surfaces practical tricks: prompt templates, self-reflection, and a model-based hallucination detector coming soon.
AI-written summary · about
chapters (8)
- 0:00Industry challenges and goals
- 3:21Understanding hallucinations
- 9:17Retrieval augmentation
- 14:46Demo: Retrieval in action
- 18:57Prompt engineering tips
- 29:10Beyond basic retrieval
- 34:14Future directions
- 35:36Q&A and conclusion
related talks
29mThe Hater's Guide to Dealing with Generative AI
15mHarnessing the Power of Generative AI for Intelligent Applications
01h 33mRAG How To Talk To Your Data
45mHow to use GPT in your projects: A Case Study
24mLLMs: Powering Lightning Fast Product Innovation
27mUnlocking the Potential of Large Language Models
50mRetrieval Augmented Generation Beyond Toy Demos
41mThe Unreliable Computer Revolution - Embracing the Double-Edged Sword of GenAI