Fundamentals of Gen AI Retrieval Dynamics

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Fundamentals of Gen AI Retrieval Dynamics

Instructor: Vasco Patrício

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

How retrieval systems represent information and compute similarity using embeddings
How to improve retrieval quality through chunking, query design, and relevance techniques
How to diagnose retrieval failures and make system behavior observable and measurable

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There are 4 modules in this course

Retrieval systems are at the core of modern Gen AI applications. But most explanations focus on tools, not understanding. This course teaches you how retrieval actually works, for RAG for LLMs, and how to design and evaluate it effectively.

You’ll learn how information is represented through embeddings, how similarity is computed, and why similarity alone is not enough to guarantee relevance. You’ll explore how choices like chunking and query processing directly impact system performance, and how to move from simple retrieval to controlled, judgment-driven systems using metadata and reranking. The course also covers how retrieval systems fail in practice (such as returning plausible but incorrect results) and how to make these failures observable, through metrics and diagnostics. What makes this course unique is its focus on mental models over tooling. Rather than teaching a specific stack, it equips you with the concepts needed to understand, debug, and improve any retrieval-based system. By the end, you will be able to reason about retrieval behavior and make better design decisions in real-world Gen AI systems.

Introduction to the course.

What's included

1 video

Build a deep understanding of how retrieval systems work by learning how information is represented, compared, and structured.

What's included

6 videos4 readings1 assignment

6 videosTotal 48 minutes

Module Intro2 minutes
How Gen AI Retrieval Works10 minutes
Representation and Similarity12 minutes
Similarity vs. Relevance11 minutes
The Effect of Chunking10 minutes
Module Conclusion2 minutes

4 readingsTotal 40 minutes

The Retrieval Stack10 minutes
Dense vs. Sparse Representation10 minutes
The Most Common Failure Mode10 minutes
Chunking Approaches10 minutes

1 assignmentTotal 30 minutes

Module-End Quiz30 minutes

Design, control, and operate production-grade retrieval systems by optimizing queries, applying judgment, and monitoring system behavior.

What's included

6 videos4 readings1 assignment

6 videosTotal 58 minutes

Module Introduction2 minutes
Query Processing and Augmentation14 minutes
Metadata for Retrieval Control13 minutes
Reranking as Judgment10 minutes
Failure Modes to Avoid15 minutes
Module Conclusion3 minutes

4 readingsTotal 40 minutes

Performing Multiple LLM Tasks10 minutes
A Few Key Fields10 minutes
Metadata in Reranking10 minutes
What Can an LLM Compensate For?10 minutes

1 assignmentTotal 30 minutes

Module-End Quiz30 minutes

Conclusion to the course.

What's included

1 video1 assignment

Instructor

Vasco Patrício

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