M6
Meaning, Not Words
Vector retrieval
Vector embeddings and approximate nearest neighbor search over the same corpus
Planned
Cohort
- Effort
- To be published
- Prerequisite
- M5
- Core concept
- Semantic similarity
What you have
Lexical search gets precision
What you gain
Semantic recall over the same documents
What you build
The module is planned. It will add a second retrieval path that complements the lexical system built earlier.
- An embedding pipeline that converts product text into vectors
- A vector index for nearest-neighbor retrieval
- A retrieval path that returns semantic candidates for the same corpus
What you learn
- How lexical retrieval and semantic retrieval answer different failure modes
- What vector similarity adds when exact terms are missing
- Why recall gains often introduce new ranking trade-offs
Artifact and workload
Primary artifact: Embedding pipeline and vector index
TestsTo be published
AssessmentsTo be published
Estimated timeTo be published
Access
This module is planned. Join the waitlist to hear when dates and access details are published.