Embeddings Explained for Builders
Understand what embeddings capture, what they miss and how to use them for search, grouping and matching.
A taste of a lesson
My semantic search returns 'refund not approved' for the query 'refund approved'. Why?
Because embeddings mostly capture topic and overall meaning, and both texts are about refund approval. The single word not changes the meaning a lot for a person but only a little for the vector, so the scores end up close. That is a known weakness, along with exact numbers and codes. Fixes include combining keyword search, reranking the top results with a model that reads query and text together, or filtering by a status field. Does your data have a structured status you could filter on?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain what an embedding is and how similarity is measured
- Use the same embedding model consistently for queries and documents
- Predict where embeddings work well and where keyword search is better
- Apply embeddings to search, clustering and duplicate detection
- Treat stored embeddings as sensitive data
Lesson plan
- 1 Text as points in space Picture embeddings as positions where similar meanings sit close together. Start
- 2 Measuring similarity Compute and interpret cosine similarity between vectors. Start
- 3 One model, used consistently Avoid mixing models and follow query and document conventions. Start
- 4 What embeddings miss Spot cases where similarity scores mislead. Start
- 5 Using embeddings Apply embeddings to search, clustering, duplicates and recommendations. Start
- 6 Cost and privacy Plan embedding costs and protect stored vectors. Start
Try asking
About this tutor
For beginners about to build search, recommendations, deduplication or retrieval for an LLM app. You learn what an embedding is, how similarity between vectors is measured, why the same embedding model must be used for queries and documents, and what embeddings capture well (topic, paraphrase, meaning across wording) and poorly (negation, exact numbers, product codes and rare names). You practise with small examples, look at real similarity scores, and see how embeddings power search, clustering and duplicate detection. The tutor also covers privacy, because embeddings of personal text are still personal data.
Reviews
4.5
2 ratingsSample
- Carlos V.Sample
Clear and honest about limits. I did not know mixing embedding models was a problem; we were about to do exactly that.
- Nora L.Sample
Predicting similarity scores before seeing them taught me more than any diagram. The negation example explained a bug in our support search.
About the teacher
Search engineer teaching embeddings, chunking, vector and keyword search, and reranking from first principles
9 tutors 374 lessons taught Sample
I come from search: indexes, ranking and the long tail of queries that make a search box look foolish. When retrieval augmented generation arrived, most of what mattered turned out to be old search problems in new clothes, so that is how I teach it. We start with how text becomes something you can compare, then how documents are split,...
See Emeka's profile and tutorsMore like this
Other tutors on the same or nearby topics.