Building LLM-Powered Recommendation Systems
With Rishabh Misra
Liked by 14 users
Duration: 2h 18m
Skill level: Intermediate
Released: 2/5/2026
Course details
Description
What is this course about?
Get a technically grounded overview of how to start building the next generation of intelligent recommender systems. Moving beyond traditional algorithms, this course shows you how to enhance existing systems by applying AI-powered techniques for embedding generation, semantic reranking, and cold start mitigation. Instructor Rishabh Misra outlines how to design sophisticated GenAI-native architectures that enable dynamic experiences such as conversational search and multimodal recommendations. The course emphasizes robust evaluation, including how to measure quality, fairness, and factual accuracy using approaches like retrieval-augmented generation (RAG). By the end, you’ll be prepared to design, evaluate, and operationalize effective and responsible GenAI recommender systems in a production environment.This course is integrated with GitHub Codespaces, a cloud-based development environment that provides full IDE functionality without requiring local setup, enabling hands-on practice from any machine.
Instructor
Who teaches this course?
Rishabh Misra is a Principal ML Engineer at Atlassian, where he leads LLM post-training and GenAI personalization initiatives.Objectives
What will I be able to do by the end of this course?
- Articulate the differences between traditional recommender systems and modern GenAI-powered approaches, including the shift to semantic understanding.
- Apply practical GenAI techniques such as embedding generation, chain-of-thought reranking, and retrieval-augmented generation (RAG) to improve performance and trustworthiness.
- Design high-level architectures for GenAI-native recommender systems, selecting appropriate models and infrastructure like vector databases.
- Develop evaluation strategies using metrics for quality, fairness, and factual accuracy.
- Create operational plans for deployment, including latency management, model monitoring, and CI/CD pipeline integration.
Audience
Who is this course for?
- Software engineers
- Data scientists
- AI and ML engineers
- Technical product managers
Prerequisites
What do I need to know before taking this course?
- Basic understanding of machine learning concepts
- Familiarity with AI and ML frameworks and tools
- Experience in software engineering or data analysis is beneficial
Skills you’ll gain
Earn a sharable certificate
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Meet the instructor
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Contents
What’s included
- Learn on the go Access on tablet and phone
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