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Kimi K3 The Complete Open-Weight LLM & AI Agent Course
Published 9/2026
Created by Dr. Florian Detzel | BI Academy
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 108 Lectures ( 9h 43m ) | Size: 11 GB
Master the open-weight Kimi K3 model: RAG, tool calling, agentic workflows, evaluation, and enterprise deployment
What you'll learn
What is Kimi K3? Learn LLM Basics Build agentic, tool-calling systems on Kimi K3, an open-weight model competitive with the closed frontier Evaluate an LLM system's accuracy, cost, and latency with your own harness instead of trusting a public benchmark Query million-token documents in a single API call, and know when retrieval (RAG) is the better choice instead Assemble a vision-based agent that compares paired images against a written policy Choose correctly between prompting, retrieval, and fine-tuning - and run a real LoRA fine-tuning job Defend a deployment's licensing, security, and data-residency posture to legal, compliance, and finance Cost optimization techniquesRequirements
No prior LLM, machine learning, or AI experience requiredDescription
Kimi K3 is Moonshot AI's open-weight model: near-frontier performance, a fraction of the usual price, and yours to build on. This course is the engineering layer on top of it: evaluating a system before you trust it, controlling what it costs, choosing correctly between prompting, retrieval, and fine-tuning, and defending a deployment to security and legal.
Every module builds toward one running case study: Avelto Rentals, a fictional car rental company with a real database, a legal policy corpus, and a year of support tickets.
In this course, you will
Query a million-token policy corpus in a single call, no vector database Build an evaluation harness before building anything that needs evaluating Build a tool-calling agent with a real MCP server, scored on accuracy Build a vision agent that checks damage photos against a written policy Cover the licensing, security, and compliance a deployment has to clear before it shipsEvaluation comes before the agent-building modules, so the same measurement harness you build early gets reused on progressively harder systems, instead of trusting a demo that only worked once. The closing module treats security, licensing, and compliance as four gates a deployment has to clear, each with its own owner and its own failure mode - the reason a working system and an approved one aren't the same thing.
Who this course is for
Developers and software engineers who want to build a real product on an LLM, not just prompt one in a chat window AI/ML engineers who know the theory and want the production side: evaluation, cost control, deployment Engineering leads and architects deciding whether an open-weight model belongs in their stack Everyone who wants to learn about Kimi K3Homepage
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