GenAI Learning Resources

This page collects supplementary resources for the LLM, multimodal AI, RAG, agentic AI, and GenAI system-design topics covered in ML Fundamentals and ML System Design.

Educative.io

Choose resources based on the gap you need to close rather than completing every course.

GenAI and LLM foundations

• Generative AI Handbook - A short introduction to LLMs, prompting, fine-tuning, vector databases, RAG, diffusion models, and agents.
• LLM Bootcamp - A deeper path through transformers, embeddings, vector databases, fine-tuning, LoRA/QLoRA, RAG, evaluation, and production trade-offs.
• Become an LLM Engineer - A broader skill path for building practical LLM applications.

RAG and production systems

• Build a RAG System with LangChain - Covers the end-to-end RAG pipeline, including embeddings, vector storage, generation, and optimization.
• Advanced RAG Techniques - Focuses on retrieval quality and more advanced RAG configurations.
• LLMOps - Covers evaluation, observability, feedback loops, reliability, scaling, latency, and cost for production LLM systems.

Agents and agentic systems

• Agentic AI Systems - Introduces agent architecture and system-design decisions for autonomous workflows.
• Agentic Design Patterns - Covers tools, retrieval, memory, routing, and evaluators as building blocks for agents.
• Build AI Agents and Multi-Agent Systems with CrewAI - A hands-on introduction to single-agent and multi-agent workflows.

Interview and system-design preparation

• Grokking the Generative AI System Design - Covers system design for text, image, speech, and video generation, including training, inference, scaling, and evaluation.
• Grokking the Machine Learning Interview - Reviews practical ML concepts and end-to-end interview case studies.
• Grokking the Machine Learning System Design Interview - Focuses on structured ML system-design reasoning, production trade-offs, and mock interviews.

Additional repositories

• Agentic AI Systems - System-design notes, implementation examples, and interview preparation for agentic AI.
• Production Level Deep Learning - Production ML and deep learning system-design material.