Vector Databases and Retrieval-Augmented Generation
Part of the Prompt Engineering course
In lesson 2 you saw why a model hallucinates: it predicts likely next tokens, it does not look facts up, and anything outside its training or its context window is a guess. In lesson 5 you learned to fill a prompt template with data before sending it. This lesson combines those ideas into the standard fix for grounding a model in real, current, private information: retrieve the right text first, then put it in the...
A model only knows two things: what it absorbed during training, and what you put in the prompt right now. It cannot see your company wiki, last week's support tickets, or a PDF you have on disk. Ask about any of those and it will produce fluent, confident, wrong text. That is the hallucination problem from lesson 2. ...
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Start this lessonVector Databases and Retrieval-Augmented GenerationWhat you'll learn
- Embeddings and vector similarity
- Chunking documents
- Vector databases and nearest-neighbor search
- Retrieval-augmented generation
- Grounding answers with citations
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All lessons in this course
11 lessons- 1What a Prompt Is20 min
- 2How Large Language Models Generate Text30 min
- 3The Five Principles of Prompting30 min
- 4Standard Practices for Text Generation35 min
- 5Prompt Templates and Function Calling35 min
- 6Vector Databases and Retrieval-Augmented Generation40 min
- 7Autonomous Agents: Memory and Tools40 min
- 8How Diffusion Models Generate Images30 min
- 9Standard Practices for Image Generation35 min
- 10Advanced Image Techniques: Inpainting, Outpainting, and ControlNet35 min
- 11Building AI-Powered Applications40 min
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