CF

AI Engineering with Go

11h 13m 5s
English
Paid

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.

Course Objectives

This course aims to equip you with the skills to build and deploy AI projects using Go. You will gain practical experience that will help in advancing your career and expanding your project portfolio.

What You Will Learn

  • Create Complete AI Projects: Develop more than three full-fledged AI projects and learn how to effectively deploy them.
  • LLM API Integration: Master the art of working with LLM APIs such as OpenAI and Claude using Go.
  • Vector Databases and Semantic Search: Implement vector databases, embeddings, and develop semantic search capabilities.
  • Model Interactions: Build interactions with models using function calling and structured queries to create seamless solutions.
  • Deploy AI Applications: Learn how to deploy AI applications to production with a robust and well-thought-out architecture.
  • Building AI Systems: Create advanced step-by-step AI systems and intelligent content processing mechanisms.

About the Author: ByteSizeGo

ByteSizeGo thumbnail

ByteSizeGo is a Go-focused course platform run by Aaron Lee, focused on the production / debugging side of Go engineering rather than language tour material. The platform's distinctive contribution is the Foundations of Debugging series, which teaches the systematic skill of debugging Go services in production rather than relying on ad-hoc print statements.

The CourseFlix listing carries six ByteSizeGo courses: Foundations of Debugging for Golang, The Ultimate Guide to Debugging With Go, The Anatomy of Go, Building Production-Ready Services with gRPC and Go, AI Engineering with Go, and The Art of Command Line Interfaces. Material is paid and aimed at working Go developers ready to deepen the operational side of their craft.

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#1: 001 Project Overview
All Course Lessons (43)
#Lesson TitleDurationAccess
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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Frequently asked questions

What prerequisites should I have before enrolling in this course?
Before enrolling in the course, you should have basic proficiency in the Go programming language, as the course focuses on integrating AI applications using Go. Familiarity with API usage and some experience in software development projects will also be beneficial, as the course involves building and deploying full-fledged AI projects.
What projects will I build during the course?
The course involves creating multiple AI projects, including a basic flashcards app that uses LLM calls, an AI agent integrated application, and systems utilizing vector databases for semantic search. These projects are designed to enhance your portfolio and provide practical experience in deploying AI applications using Go.
Who is the target audience for this course?
This course is aimed at software developers and engineers who want to expand their skills in AI and machine learning by using the Go programming language. It is suitable for those interested in learning how to integrate AI into practical applications and deploy them effectively.
How does this course differ in depth compared to similar AI courses?
Unlike many AI courses that focus on Python, this course emphasizes using Go for AI application development. It provides hands-on experience with LLM API integration, vector databases, and deploying AI systems, offering a unique angle by leveraging Go-specific capabilities for AI engineering.
What specific tools or platforms will I learn to use?
The course covers tools and platforms such as LLM APIs like OpenAI and Claude, vector databases for semantic search, and tools for deploying AI applications in a production environment. You will also learn to scaffold APIs and create frontend components as part of the projects.
What topics are not covered in this course?
The course does not cover the foundational theories of AI or machine learning in depth. Instead, it focuses on the practical application of AI using Go. It also does not provide extensive coverage of Python-based AI tools or frameworks, as its primary focus is on Go.
How much time should I expect to commit to this course?
The course comprises 43 lessons and requires a significant time commitment to complete. While the exact runtime is not specified, expect to spend additional time on building projects, engaging with course materials, and applying concepts in practical assignments to gain the most benefit.