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Ailurn
AI & Data

Building Production LLM Applications with AI SDK & TypeScript

Designed for full-stack developers, this practical course teaches you how to build production-grade AI features, agentic workflows, and structured LLM integrations using TypeScript and the AI SDK. You will gain hands-on skills to implement robust streaming, tool calling, and end-to-end application architectures.

28 lessons · about 11 hours · by Chukwuma Ebube · 0 forks

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Configuring Next.js App Router for AI Workloads

Configure Next.js 15+ App Router projects for AI integrations and server-side execution boundaries

Syllabus

28 lessons across chapters
1

Module 1: Modern AI App Architecture & Core Setup

4 lessons

  1. Lesson 1

    Configuring Next.js App Router for AI Workloads

    Configure Next.js 15+ App Router projects for AI integrations and server-side execution boundaries

  2. Lesson 2

    Setting Up Vercel AI Gateway & Provider Registries

    Initialize the Vercel AI Gateway and provider registries for multi-model fallback and unified routing

  3. Lesson 3

    Isolating API Keys & Environment Security

    Secure environment variables and isolate API keys against client-side exposure

  4. Lesson 4

    Establishing Strict TypeScript AI Types

    Structure TypeScript configuration files and custom type definitions for AI payloads

2

Module 2: Text & Type-Safe Structured Data Generation

6 lessons

  1. Lesson 5

    Generating Text with Server-Side Core Functions

    Implement `generateText` for non-streaming server-side text generation tasks

  2. Lesson 6

    Streaming Text with Server-Sent Events

    Implement `streamText` to deliver real-time token streaming from server actions and route handlers

  3. Lesson 7

    Leveraging Model Reasoning & System Prompts

    Configure reasoning options and parameters for frontier reasoning models

  4. Lesson 8

    Designing Zod Schemas for Structured Outputs

    Define robust Zod schemas for type-safe data extraction and classification

  5. Lesson 9

    Extracting Typed Data with `generateObject` and `streamObject`

    Implement `generateObject` and `streamObject` to parse structured JSON reliably from LLM outputs

  6. Lesson 10

    Processing Multimodal Inputs & File Parts

    Handle multi-modal image buffers and PDF file attachments securely in text generation requests

3

Module 3: Frontend Streaming & UI Integration

4 lessons

  1. Lesson 11

    Integrating `@ai-sdk/react` Hooks

    Configure `@ai-sdk/react` hooks (`useChat` and `useCompletion`) within Next.js client components

  2. Lesson 12

    Managing Chat State & UI Lifecycle

    Manage chat history states, loading indicators, and error boundaries on the client interface

  3. Lesson 13

    Optimizing Streaming UI with Smooth Rendering

    Optimize rendering performance and prevent UI stuttering using `smoothStream`

  4. Lesson 14

    Building Rich Message Rendering Components

    Build custom chat message rendering components supporting markdown and code blocks

4

Module 4: Tool Calling & Function Execution

4 lessons

  1. Lesson 15

    Defining Server-Side Tools with Zod Schemas

    Define server-side TypeScript functions as executable tools using Zod parameter validation

  2. Lesson 16

    Handling Multi-Step Agent Tool Loops

    Execute multi-step agent tool loops where the LLM automatically invokes and responds to tool outputs

  3. Lesson 17

    Rendering Client-Side Tool UIs

    Render custom client-side UI components for active tool executions and function results

  4. Lesson 18

    Implementing Human-in-the-Loop Tool Approvals

    Implement human-in-the-loop approval workflows for sensitive tool execution steps

5

Module 5: Retrieval-Augmented Generation (RAG)

5 lessons

  1. Lesson 19

    Building Text Embedding & Chunking Pipelines

    Design text embedding pipelines and chunking strategies using AI SDK embedding models

  2. Lesson 20

    Configuring Vector Storage with PostgreSQL & Drizzle

    Connect PostgreSQL with Drizzle ORM to store and index high-dimensional vector embeddings

  3. Lesson 21

    Executing Semantic Similarity Searches

    Execute semantic similarity searches against stored vector embeddings

  4. Lesson 22

    Integrating Retrieval Tools into Chat Generation

    Expose vector search logic as a query tool to `streamText` for dynamic context injection

  5. Lesson 23

    Constructing End-to-End RAG Workflows

    Build end-to-end RAG question-answering workflows with document citation tracking

6

Module 6: Production Agents, Workflows & Telemetry

5 lessons

  1. Lesson 24

    Building Autonomous Agent Architectures

    Construct multi-step autonomous agents using AI SDK agentic primitives and iteration limits

  2. Lesson 25

    Handling Long-Running Tasks with Durable Workflows

    Implement durable workflows and background agents to bypass serverless function timeouts

  3. Lesson 26

    Configuring Production Telemetry & Tracing

    Configure production telemetry, tracing channels, and error tracking for AI applications

  4. Lesson 27

    Implementing Resilience, Caching & Fallbacks

    Implement rate limiting, caching, and fallback strategies to handle provider outages gracefully

  5. Lesson 28

    Deploying Production AI Apps to Edge Runtimes

    Deploy production AI applications to Vercel and configure edge-native streaming runtimes

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