Module 3: Embeddings Deep Dive Slides

Slide Outline

1. Embeddings Deep Dive - Embedding models, optimization strategies, and choosing the right model for your use case
2. Learning Objectives - 4 outcomes for this module
3. Why This Module Matters - If your embeddings are bad, your retrieval is bad, and your RAG answers are bad. No amount of prompt engineering fixes p
4. How Embedding Models Work - Lesson section from the full module
5. Comparing Embedding Models - Lesson section from the full module
6. Embedding Optimization - Lesson section from the full module
7. Common Mistakes to Avoid - 4 mistakes covered
8. Hands-On Labs - 2 hands-on labs
9. Key Takeaways - 5 points to remember

Learning Objectives

• Understand how text embedding models work
• Compare embedding models and their tradeoffs
• Optimize embeddings for production performance
• Choose the right embedding strategy for your data

Why This Module Matters

If your embeddings are bad, your retrieval is bad, and your RAG answers are bad. No amount of prompt engineering fixes poor embeddings. This module teaches you to choose, optimize, and evaluate the component that determines 80% of your RAG quality.

Common Mistakes

• Using the cheapest/fastest embedding model without benchmarking quality
• Re-embedding entire corpus on every update instead of incremental embedding
• Mixing embedding models — query and document MUST use the same model
• Not normalizing vectors before cosine similarity calculation

Key Takeaways

• Embedding quality directly determines retrieval quality
• Smaller models (MiniLM) are fast but less accurate; larger models (mpnet) are better but slower
• Batch processing and caching are essential for production throughput
• Quantization reduces storage 4x with minimal quality loss
• Choose your embedding model based on benchmarks on YOUR data, not general leaderboards

Hands-On Labs

1. Generate and Compare Embeddings

   Explore how different models embed the same text.

   30 min - Beginner

   • Embed identical sentences with 3 different models
   • Compare vector dimensions and similarity scores
   • Measure latency and throughput per model
   • Visualize embedding clusters with t-SNE

   View lab files on GitHub

2. Embedding Model Selection

   Choose the right model for your use case.

   25 min - Intermediate

   • Benchmark retrieval quality on a test dataset
   • Compare small vs large models on precision/recall
   • Measure latency at different batch sizes
   • Document model selection decision for production

   View lab files on GitHub