Plan de estudios

Solo títulos de módulos y lecciones. El contenido completo se desbloquea tras la inscripción.

1. Module M01

   1. Lesson M01-L01 — Embeddings as retrieval representations
   2. Lesson M01-L02 — Similarity search mental model
   3. Lesson M01-L03 — Semantic vs lexical retrieval
   4. Lesson M01-L04 — Indexing: exact vs approximate nearest neighbor

2. Module M02

   1. Lesson M02-L01 — Document and chunk identity
   2. Lesson M02-L02 — Namespaces, collections, and tenancy
   3. Lesson M02-L03 — Metadata design and filtering
   4. Lesson M02-L04 — Ingest lifecycle: upsert, delete, version, migrate

3. Module M03

   1. Lesson M03-L01 — Chunking strategies and overlap
   2. Lesson M03-L02 — Parent-child and hierarchical retrieval concepts
   3. Lesson M03-L03 — Hybrid retrieval, sparse+dense, and reranking hooks
   4. Lesson M03-L04 — Top-k, diversity, query transforms, debugging

4. Module M04

   1. Lesson M04-L01 — Evaluation datasets and recall-oriented metrics
   2. Lesson M04-L02 — Latency, cost, and indexing lifecycle ops
   3. Lesson M04-L03 — Observability, security, privacy, backups
   4. Lesson M04-L04 — Ship JP-JA16 Vector Lab