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AI SDK v5 Crash Course

2h 45m 17s
English
Paid

100% TypeScript. 100% Production-ready. 0% hype. Only real tools and experience. Integration with LLM has finally reached a new level - now it's truly worth your time. Just a year ago, development for LLM was chaos: hacks, unstable APIs, outdated interfaces, and endless reworks. Now everything is different thanks to the AI SDK.

Why AI SDK v5 is a Game-Changer

AI SDK v5 transforms the development landscape:

  • Write once - works with any LLM provider.
  • Modern patterns and tools replace homemade solutions.
  • Production-ready, secure, and elegant TypeScript code.

If you've been waiting for stable APIs, production-ready tools, and clear architectural patterns - the moment has come.

Course Overview

In this intensive course, you'll learn how to add LLM-based features to your TypeScript applications efficiently.

The course includes 89 short videos and 57 practical exercises across 10 modules. Everything is practical, dynamic, and concise.

Key Learning Objectives

  • Basic principles of LLM including tokens, context, and windows.
  • Key architectural patterns: agents, workflows, tool-calling, and more.
  • Production tools: observability, logging, error handling.
  • Eval-Driven Development to tackle model hallucinations.
  • Effective use of AI SDK v5.

Projects You Will Create

  • Chat title generator with a dataset for evaluation.
  • Workflow for generating and enhancing messages in Slack.
  • Guardrail to protect your application.
  • Request router for sending simple tasks to cost-effective models.
  • Exploratory agent for information searching on the web.

All videos are short (2–3 minutes) and exercises are easy yet comprehensive, allowing you to learn even with a busy schedule.

The Power of AI SDK v5

AI SDK v5 is not just a library. It's a unified engineering platform that saves months of development, simplifies maintenance, and makes your AI applications as reliable as the rest of your code.

More than 3.5 million downloads per week - and it's not by chance:

  • Fully open source (Apache 2.0).
  • Supports all major frameworks and LLMs.
  • Unified interface for all models and providers.
  • Easy integration with OpenTelemetry, Langfuse, Braintrust, and other tools.
  • Works everywhere: Node, Deno, Bun.

AI SDK v5 makes LLM application development as modern as everything else in TypeScript. Join us and start creating smart, production-ready features today.

Additional

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

About the Author: Matt Pocock

Matt Pocock thumbnail

Matt Pocock is the British developer who has become one of the most recognizable voices in the TypeScript community. His Total TypeScript course series — built around the "wizard" interactive challenge format — is widely cited as the deepest TypeScript-specific learning material available, particularly for the type-system features (conditional types, mapped types, template literal types) that most JavaScript-to-TypeScript migrations skip.

Matt's content reaches an audience that includes the senior engineers writing TypeScript libraries others depend on. The Total TypeScript Pro Essentials and Total TypeScript Advanced courses are standard recommendations for engineers who want to write the kind of TypeScript that catches real bugs at compile time rather than just satisfying a linter.

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