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.
Want to learn Prompt Engineering Vector Databases and Retrieval-Augmented Generation in a real environment?
Click below, get a fresh Linux box with Prompt Engineering ready to go, and work through Vector Databases and Retrieval-Augmented Generation hands-on with an AI tutor that knows your code.
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