IntermediateAILLMRAG
AI Engineering: LLMs, RAG & Agents
Build production AI apps — not demos. LLM fundamentals, prompting, RAG, agents, and the evals that make it all shippable.
4.9 6 sections 26 lessons ~4h read
AI Engineering: LLMs, RAG & Agents
₹399₹249984% off
- 3 free preview lessons
- Lifetime access + future updates
- Read in-browser, progress saved
- Interview-focused, visual-first
What you'll learn
An accurate mental model of LLMs: tokens, embeddings, attention, and the parameters you tune daily
Prompt engineering that works: the 5-part skeleton, few-shot, chain-of-thought, and schema-enforced structured output
Build a production RAG pipeline end to end — chunking, vector search, hybrid retrieval, reranking, and evaluation
Design and ship agents: the tool-calling loop, planning, reflection, verifier tools, and multi-agent systems
Guardrails for real deployments: budgets, sandboxes, human-in-the-loop approval, and prompt-injection defense
Evaluate AI systems like an engineer: assertions, LLM-as-judge, golden sets, and production observability
About this course
A practical, visual course for developers who want to build real AI products. Start with an accurate mental model of how LLMs work, master prompting and structured output, build a production RAG pipeline (chunking, embeddings, hybrid search, reranking, evaluation), and learn to build agents that use tools safely. Ends with a full capstone project that ties every section together. Provider-agnostic — the concepts transfer across every model and API.
Curriculum
6 sections · 26 lessons · ~4h- How LLMs actually work (the honest mental model)Free 7m
- Tokens & embeddings: cost, limits, and meaning-as-mathFree 8m
- Transformers & attention, without the calculusFree 8m
- Temperature, top-p & the API knobs that matter 7m
- Choosing a model: the axes and the algorithm 7m
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