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• date: 6 September 2026
• posted by: voska89

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 techniques

Requirements

❗ No prior LLM, machine learning, or AI experience required

Description

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 ships
Evaluation 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 K3

Homepage

https://www.udemy.com/course/kimi-k3-the-complete-open-weight-llm-ai-agent-course

   
   
   

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