Spark LampHow Do You Give AI Its Own Knowledge?
Why models don’t know your facts, how meaning search and vector databases find them, how RAG feeds them to the model, and when fine-tuning is the better tool.
Modules
Each module explains one idea in plain words, lets you try it, then checks what stuck.
- 01
Why LLMs Don't Know Everything
Understand the three kinds of knowledge a model simply doesn’t have, and the idea behind fixing that.
- 02
How AI Searches by Meaning Instead of Keywords
See why searching by meaning finds answers that keyword search misses, and where keywords still win.
- 03
Embeddings: Turning Documents Into Searchable Vectors
See how whole documents and questions become vectors on one shared map of meaning.
- 04
What Is a Vector Database?
Learn what a vector database stores, what it’s asked to do, and why ordinary databases needed help.
- 05
How Vector Search Finds the Closest Matches
See how vector databases find the closest matches among millions in milliseconds, and what they trade for it.
- 06
RAG: Giving AI Access to External Knowledge
Put the pieces together: retrieve the right passages, add them to the prompt, and let the model answer from them.
- 07
How to Turn Documents Into AI-Readable Data
Learn why real documents need cleaning before AI can use them, and what the cleaning involves.
- 08
Chunking: Why Document Size Matters
See why documents are split into chunks, and how the size and placement of the splits decide what can be found.
- 09
Inside a Complete RAG Pipeline
See a complete RAG system end to end, and learn to trace a bad answer back to the stage that caused it.
- 10
Hybrid Search and Reranking: Finding Better Answers
Learn how combining keyword and meaning search, then reranking, fixes many retrieval failures.
- 11
RAG vs Fine-Tuning: Which Should You Use?
Know when to reach for RAG, when for fine-tuning, when for both, and when a better prompt is enough.