From the course: Fundamentals of AI Engineering: Principles and Practical Applications
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Introduction to retrieval engineering
From the course: Fundamentals of AI Engineering: Principles and Practical Applications
Introduction to retrieval engineering
- [Instructor] In this chapter, we're going to bring together everything we've learned so far to build production ready retrieval pipelines that power efficient RAG systems. Throughout this course, we've explored embeddings with sentence transformers, worked with vector databases using Chroma and extracted documents with llama index. These are powerful tools on their own, but in real world applications, we need to combine them thoughtfully to create truly effective retrieval systems. Let's start by understanding some crucial limitations in what we've covered so far. While vector search is excellent at capturing semantic meaning, it has significant blind spots. First, it struggles with specialized terminology and exact matches. If you're searching for technical jargon or specific product names, pure vector search might miss them. Second, the dimensionality constraints of embedding spaces affect precision. Even with models that use hundreds of dimensions, there's only so much semantic…
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