CF

AI SDK v6 Crash Course

3h 2m 47s
English
Paid

Learn how to build solid AI features with clear patterns and stable tools. Work with LLMs in a way that feels normal for modern TypeScript. A year ago this was hard. Tools were unstable and patterns were unclear. The AI SDK v6 fixes this and gives you a clean path.

Why AI SDK v6 Matters

AI SDK v6 gives you one simple way to work with many LLM providers.

  • Write code once and switch models without rewrites.
  • Use clear patterns instead of hacks or custom wrappers.
  • Get strong TypeScript types and safe production defaults.

You now have stable APIs, clear docs, and tools that work well in real systems.

Course Overview

You will learn how to add LLM features to your TypeScript apps without guesswork.

The course has 89 short videos and 57 hands-on tasks in 10 modules. Every lesson is clear and focused.

What You Will Learn

  • How tokens, context, and context windows work.
  • Core patterns like agents, workflows, and tool-calling.
  • Production needs such as logs, metrics, and error handling.
  • How to reduce hallucinations with eval-driven checks.
  • How to use AI SDK v6 in real apps.

Projects You Will Build

  • A chat title generator with an eval dataset.
  • A Slack message workflow that edits and improves text.
  • A guardrail that blocks unsafe or broken prompts.
  • A router that sends simple tasks to cheaper models.
  • An agent that searches the web for clear answers.

Videos stay short at 2–3 minutes. Tasks fit into a busy day and give you steady progress.

The Power of AI SDK v6

AI SDK v6 is more than a helper library. It gives you one design that works across models and providers. This cuts setup time and reduces long-term issues.

It has millions of weekly downloads because it is stable and easy to use.

  • Open source under Apache 2.0.
  • Works with major LLMs and frameworks.
  • One interface for all providers.
  • Simple links to OpenTelemetry, Langfuse, Braintrust, and more.
  • Runs on Node, Deno, and Bun.

AI SDK v6 makes LLM work feel like normal TypeScript. You can build clear, reliable features without extra tools or custom code.

Additional

https://github.com/ai-hero-dev/ai-sdk-v5-crash-course

About the Author: Matt Pocock

Matt Pocock thumbnail

Matt Pocock is a UK-based developer and the founder of Total TypeScript — one of the most authoritative paid course platforms on the TypeScript language. He was previously a developer-experience engineer at Vercel and is widely cited as one of the clearest teachers of TypeScript's deeper type-system patterns. His Twitter / X presence is one of the largest single-language educational accounts in the JavaScript ecosystem.

His CourseFlix listing carries four Matt Pocock courses: Total TypeScript — Professional TypeScript Training (the platform's flagship comprehensive course), TypeScript Pro Essentials, AI SDK v5 Crash Course, and Build Your Own AI Personal Assistant in TypeScript. The TypeScript material is taught at the level of a working senior engineer who routinely uses the type system as a design tool, not just type annotations.

Material is paid; Total TypeScript runs on per-course pricing on the original platform. Courses are aimed at intermediate-and-up TypeScript developers.

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#1: 000 V5 to V6 Breaking Changes
All Course Lessons (95)
#Lesson TitleDurationAccess
1
000 V5 to V6 Breaking Changes Demo
01:46
2
001 What is the AI SDK
02:01
3
002 How To Take This Course
02:27
4
003 Choosing Your Model
01:51
5
004 Generating Text
00:53
6
005 Generating Text Solution
00:55
7
006 Streaming Text To The Terminal
01:07
8
007 Stream Text to Terminal Solution
00:33
9
008 UI Message Streams
01:34
10
009 Streaming To A UI
02:36
11
010 Stream Text to UI Solution
02:03
12
011 System Prompts
01:26
13
012 Passing Images and Files
01:44
14
013 Passing Images and Files Solution
01:30
15
014 Streaming Objects via Output
01:23
16
015 Streaming Objects via Output
02:32
17
016 Tokens
01:36
18
017 Tracking Token Usage
00:34
19
018 Usage Solution
00:54
20
019 Representing Data As Tokens
01:34
21
020 Context Window
01:51
22
021 Prompt Caching
02:16
23
022 Calling Tools
02:13
24
023 Tool Calling Solution
01:12
25
024 Message Parts
03:14
26
025 Showing Tools in the Frontend
02:32
27
026 Showing Tools in the Frontend Solution
02:10
28
027 Calling MCP Servers via stdio
02:10
29
028 MCP via stdio Solution
01:25
30
029 Calling MCP Servers via HTTP
01:16
31
030 Waiting For Streams To Finish
04:11
32
031 Passing Chat ID's To The API
01:58
33
032 Pass Chat ID to the API Solution
01:23
34
033 Persisting Chat Messages
03:05
35
034 Persistence Solution
01:44
36
035 Persisting Messages In Postgres
03:44
37
036 The Template
03:16
38
037 Basic Prompting
00:55
39
038 Basic Prompting Solution
01:02
40
039 Exemplars
00:42
41
040 Exemplars Solution
01:09
42
041 Retrieval
01:23
43
042 Retrieval Solution
01:31
44
043 Chain of Thought
01:58
45
044 Chain of Thought Solution
02:04
46
045 Evalite Basics
02:13
47
046 Evalite Basics Solution
01:10
48
047 Deterministic Evals
01:33
49
048 Deterministic Eval Solution
01:27
50
049 LLM-as-a-judge Evals
03:29
51
050 LLM as a Judge Solution
01:09
52
051 Dataset Management
01:55
53
052 Chat Title Generation
01:56
54
053 Chat Title Generation Solution
01:20
55
054 How Do I Know My Dataset Is Good_
02:23
56
055 Langfuse Basics
02:56
57
056 Langfuse Basics Solution
01:52
58
057 Custom Data Parts
02:52
59
058 Custom Data Parts Solution
01:37
60
059 Streaming Objects To Custom Data Parts
01:21
61
060 Custom Data Parts with Stream Object Solution
02:01
62
061 Message Metadata
01:30
63
062 Message Metadata Solution
01:10
64
063 Error Handling
01:54
65
064 Error Handling Solution
00:55
66
065 Building A Workflow
01:39
67
066 Workflow Solution
01:16
68
067 Streaming Custom Data to the Frontend
02:19
69
068 Streaming Custom Data to the Frontend Solution
01:39
70
069 Creating Your Own Loop
02:14
71
070 Creating Your Own Loop Solution
01:31
72
071 Breaking the Loop Early
02:20
73
072 Breaking the Loop Early Solution
02:09
74
073 Guardrails
02:38
75
074 Guardrails Solution
02:43
76
075 Model Router
01:34
77
076 Model Router Solution
02:03
78
077 Comparing Multiple Outputs
03:39
79
078 Comparing Multiple Outputs Solution
01:44
80
079 Research Workflow
02:55
81
080 Research Workflow Solution
03:14
82
081 UI Messages vs Model Messages
01:12
83
082 Defining Tools
01:38
84
083 consumeStream
01:57
85
084 Custom Data Parts -
01:21
86
085 Streaming Custom Data Parts To The Frontend
01:12
87
086 Using ID's In Custom Data Parts
01:17
88
087 Message Metadata -
02:14
89
088 Streaming Text Parts By Hand
01:17
90
089 Start and Finish Parts
03:11
91
090 Generating Objects via Output
01:18
92
091 Generating Objects via Output Solution
02:27
93
092 Devtools Basics
02:40
94
093 Tool Loop Agent
03:50
95
094 Tool Approval
03:30
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Frequently asked questions

What prerequisites should I have before starting this course?
Before enrolling, you should be comfortable with TypeScript as the course focuses on integrating AI SDK v6 with TypeScript applications. Familiarity with basic concepts of large language models (LLMs) will also be beneficial since the course covers how to work with different LLM providers using the SDK. Understanding of general programming principles and API usage is also recommended to effectively grasp the course material.
What kind of projects will I build during this course?
The course includes hands-on projects such as building a chat title generator using an evaluation dataset, creating a Slack message workflow for text editing and improvement, developing a guardrail to block unsafe prompts, implementing a router to direct tasks to cost-effective models, and designing an agent that searches the web for answers. These projects are designed to provide practical experience with AI SDK v6 in real-world applications.
Who is the target audience for this course?
This course is geared towards developers who want to integrate AI features into their TypeScript applications using AI SDK v6. It is ideal for software engineers and tech enthusiasts seeking to leverage stable tools and patterns for working with LLMs. Professionals interested in enhancing their applications with AI capabilities without the complexity of building custom wrappers will find this course particularly useful.
How does the depth of this course compare to similar AI SDK courses?
The course provides a focused exploration of AI SDK v6, emphasizing stable tools and clear patterns for LLM integration. With 89 short videos and 57 hands-on tasks, it offers a comprehensive look at core patterns like agents, workflows, and tool-calling, as well as production needs such as logging and error handling. While other courses may offer broader overviews, this course specializes in practical implementation with TypeScript, ensuring that learners gain actionable skills.
What specific tooling does the course cover?
The course thoroughly covers AI SDK v6 and its integration with TypeScript applications. Key topics include working with tokens, context windows, and production aspects like logs and metrics. Lessons also focus on specific tooling such as agents, workflows, tool-calling, and how to use eval-driven checks to reduce hallucinations. Additionally, the course addresses how to seamlessly switch between LLM providers without code rewrites.
What topics are not covered in this course?
The course does not cover foundational AI and machine learning concepts, as it assumes some familiarity with these areas. It focuses specifically on practical implementation using AI SDK v6 rather than the underlying theoretical aspects of AI or deep learning. Additionally, it doesn't delve into other programming languages beyond TypeScript or cover non-LLM based AI technologies.
How much time should I expect to commit to this course?
The course is designed to fit into a busy schedule, with 89 short videos averaging 2–3 minutes each and 57 hands-on tasks. While the total runtime is not specified, the course is structured in 10 modules, allowing for steady progress. The tasks are designed to be manageable on a daily basis, providing consistent learning without overwhelming time commitments.