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Embeddings Deep Dive Guide
Master embeddings for production AI: implement semantic search from scratch with numpy, compare OpenAI vs open-source models, optimize with batch processing and caching, evaluate quality with precision@k and MRR, and build a complete production-ready semantic search engine.
- 64
- lessons
- 8
- modules
- English · Spanish
- available in
- Yes
- certificate
- Free
- access
Outcomes
What you'll be able to do
- Understand embeddings architecture: transformers, tokenization, pooling, normalization
- Compare embedding models objectively: OpenAI, SBERT, BGE with MTEB benchmarks
- Implement semantic search from scratch with numpy (no black-box libraries)
- Apply chunking strategies: fixed-size, semantic, recursive — and evaluate which works best
- Master distance metrics: cosine similarity, euclidean, dot product — know when to use each
- Perform embedding operations: arithmetic, interpolation, composition, clustering
- Optimize for production: caching, error handling, monitoring, scaling, cost control
- Evaluate embedding quality with golden datasets, precision@k, recall@k, MRR
- Build a complete semantic search engine with FAISS, query expansion and reranking
Before you start
What you need to bring
It's for you if...
- AI Engineers implementing semantic search or RAG who need deep embedding understanding
- Backend developers transitioning to AI who want production-ready skills from day one
- Professionals who use `langchain.embeddings` but can't debug when things break
- Teams choosing embedding models who need an objective comparison framework
- Students in the AI Engineering Path preparing for vector databases and advanced RAG
Requirements and materials
- Python basics + OOP (loops, functions, classes)
- REST APIs and HTTP fundamentals (requests, JSON)
- OpenAI API access (or open-source alternative)
- Vector concepts from AI Semantics Guide (#5) — what vectors are, cosine similarity, semantic search concept
- Basic numpy (arrays, operations)
Content
The syllabus, module by module
Open any of them to see its lessons.
- Welcome to Module 2: How Do Embeddings Work?
- Transformer Encoders: The Architecture of Modern Embeddings
- Tokenization: From Text to Tokens
- Contextualization: Embeddings That Understand Context
- Pooling Strategies: From Multiple Tokens to One Vector
- Normalization: Scaling Embeddings to Unit Magnitude
- OpenAI API Advanced: Parameters and Production Patterns
- Mini-Project: Robust API Client for Production
- Introduction to Module 3: Embedding Models Compared
- OpenAI Embeddings Models: Deep Dive Comparison
- Open-Source Embeddings: Self-Hosted Alternatives
- MTEB: Massive Text Embedding Benchmark
- Latency and Throughput: Real Performance
- Cost Analysis: The Economics of Embeddings
- Domain-Specific and Multilingual Embeddings
- Mini-Project: Benchmark Framework for Embedding Models
- Introduction to Module 4: Evaluation and Chunking Strategies
- Fixed-Size Chunking: Practical Implementation
- Semantic Chunking: Respecting Document Structure
- Retrieval Metrics: Measuring RAG Performance
- Creating Evaluation Datasets for RAG
- A/B Testing Chunking Strategies
- Advanced Chunking Patterns
- Mini-Project: RAG System with Intelligent Chunking
- Module 8: Final Capstone Project - Complete RAG System
- Document Ingestion Pipeline
- Chunking & Embedding Pipeline Integration
- Vector Search with FAISS
- Evaluation Framework: Measuring RAG Performance
- Query Expansion & Reranking: Two-Stage Retrieval
- Production Deployment: Docker, FastAPI, Kubernetes
- Conclusions and Next Steps
Where it fits
This guide is part of something bigger
It's studied inside these programs, with support and dates.
Common questions
What people usually ask
No limit. It's a free guide: come in whenever you like, as often as you like.
No. Modules run from easier to harder, but you can jump to the one you need. Progress is saved per lesson.
Whatever is needed is listed under “What you need to bring”, above. If nothing is listed there, you can start from zero.
In the Club's WhatsApp group, and every two weeks there's a live with an instructor where questions get worked through.
Yes. It's issued automatically once you finish every lesson, with a verifiable code you can share on LinkedIn.
No. This guide is self-paced with no dates. The bootcamp is live, by cohort, with work someone reviews.
Start whenever you like
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