Module 3 of 6Lesson 2 of 2~16 min
Understand embeddings and semantic search
Understanding semantic search helps a PM predict which questions the assistant will handle well and which it will miss. With no math, the lesson explains how embeddings match texts by meaning, which settings are product decisions, and which weaknesses to anticipate in the specs.
Lesson objective
By the end of this lesson, you will be able to explain how semantic search retrieves passages, list the product decisions it involves (model, number of passages, relevance threshold) and anticipate its weak spots.
Questions this lesson answers
- How does semantic search work in a RAG system?
- What product decisions come with choosing an embedding model?
- Why does semantic search miss exact error codes or reference numbers?
Topics covered
- embeddings
- semantic search
- vector database
- relevance threshold
In the glossary
Where it fits
Chunk and index
How do documents become passages that retrieval can find?
Lessons in this module
- Choose a chunking strategy
- Understand embeddings and semantic search (this lesson)
What you will learn in this module
This lesson is part of the course Design a RAG architecture that fits your product
- Specify the source inventory, exclusions, the metadata to capture and the document chunking strategy.
- Design retrieval: semantic, keyword or hybrid search, reranking, contextual retrieval, number of passages and relevance threshold.
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