AI Engineering with Go
11h 13m 5s
English
Paid
Course description
Who said that AI applications should only be developed in Python? In this course, you will learn how to practically integrate AI into real applications using Go—starting from the basic use of LLM and gradually moving to the creation of dynamic, intelligent, and autonomous AI agents. Each module of the course is result-oriented: you will not just study the technologies, but create complete projects, enhancing your portfolio and strengthening your skills.
Read more about the course
During the course you will:
- Create more than three full-fledged AI projects and deploy them;
- Learn how to work with LLM API (OpenAI, Claude) using Go;
- Implement vector databases, embeddings, and semantic search;
- Learn to build interactions with models using function calling and structured queries;
- Deploy AI applications to production with a well-thought-out architecture;
- Create step-by-step AI systems and intelligent content processing mechanisms.
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/ #1: 001 Project Overview
All Course Lessons (43)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | 001 Project Overview Demo | 02:10 | |
| 2 | 002 Setting Up Your Development Environment | 05:15 | |
| 3 | 003 Initializing Project From Template and Deploying | 14:50 | |
| 4 | 004 Introducing the Project and Building Basic Flashcards App | 34:17 | |
| 5 | 005 Making the First LLM Call | 13:51 | |
| 6 | 006 Adding AI to Our Project | 14:18 | |
| 7 | 007 Scaffolding our API to make LLM Calls | 28:16 | |
| 8 | 008 Improving our Prompts | 15:01 | |
| 9 | 009 Building the Frontend | 12:36 | |
| 10 | 010 Deploying the Frontend | 05:29 | |
| 11 | 011 Ingesting Real Notes | 07:12 | |
| 12 | 012 Polishing the Project | 27:18 | |
| 13 | 013 Wrap Up and Challenges | 01:27 | |
| 14 | 014 BONUS LLM Streaming | 22:00 | |
| 15 | 015 Project Overview - | 03:18 | |
| 16 | 016 Function Calling Intro | 17:25 | |
| 17 | 017 Using Function Calls in Projects Part 1 | 36:07 | |
| 18 | 018 Using Function Calls in Projects Part 2 | 09:13 | |
| 19 | 019 Implementing the Frontend | 12:34 | |
| 20 | 020 Adding Polish | 04:12 | |
| 21 | 021 Project Intro -- | 04:00 | |
| 22 | 022 Introduction to Vector DBs | 07:31 | |
| 23 | 023 Vector DB Example and Demo | 19:53 | |
| 24 | 024 Vector DB Crash Course | 22:55 | |
| 25 | 025 Quiz v2 - API Scaffold | 14:25 | |
| 26 | 026 Index Notes | 20:07 | |
| 27 | 027 Implementing Our New Quiz API | 23:46 | |
| 28 | 028 Frontend and Polish | 19:46 | |
| 29 | 029 Deploying to Production | 07:26 | |
| 30 | 030 Project Overview --- | 05:59 | |
| 31 | 031 AI Agents Overview | 08:49 | |
| 32 | 032 Let's build an AI agent | 19:31 | |
| 33 | 033 Integrating an AI agent into our app | 28:57 | |
| 34 | 034 Implementing the frontend -- | 05:08 | |
| 35 | 035 Refining our agent prompt + intro to tools | 15:24 | |
| 36 | 036 Building tools | 21:59 | |
| 37 | 037 Polish | 33:19 | |
| 38 | 038 Deploying to production -- | 03:21 | |
| 39 | 039 How Does MCP Work | 21:02 | |
| 40 | 040 MCP Servers in Practice | 16:56 | |
| 41 | 041 Building MCP Servers | 20:27 | |
| 42 | 042 Integrating MCP Servers in Your App | 34:09 | |
| 43 | 043 MCP Best Practices | 11:26 |
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