Look at what you actually built:
Foundations:
- ✅ Data extraction script comparing
generateText vs generateObject
- ✅ Model comparison tool understanding speed vs quality tradeoffs
Invisible AI Features:
- ✅ Text classifier that categorizes support tickets by type and urgency
- ✅ Summarization that condenses conversations into actionable insights
- ✅ Data extractor that parses natural language into structured appointments
- ✅ Professional UI components generated with v0 in seconds
Full-Stack Chatbot:
- ✅ Streaming chat interface with
useChat and streamText
- ✅ Professional UI upgrade with AI Elements components
- ✅ System prompts giving your AI consistent personality
- ✅ Weather tool integration showing real-time data fetching
- ✅ Multi-step conversations that chain tool calls and synthesis
- ✅ Generative UI rendering custom React components from tool results
This isn't toy code. These are production patterns used by real companies.
You learned the critical patterns that power most AI applications:
- Structured Extraction - Turn messy text into clean JSON with
generateObject and Zod schemas
- Streaming Interfaces - Keep users engaged while AI thinks with
streamText and useChat
- Tool Orchestration - Let AI call functions and APIs to extend beyond text generation
- Component Systems - Professional UI with AI Elements instead of building from scratch
- Multi-Step Workflows - Chain multiple tool calls with natural language synthesis
These patterns are the foundation. Most AI features are variations or combinations of them.
You have working code for every pattern. Pick ONE and ship it this week:
Quick Wins (1-2 hours):
- Add the classification script to your support ticket workflow
- Drop the summarization Server Action into an existing Next.js app
- Use the extraction pattern for any form with natural language input
Medium Projects (1-2 days):
- Build a support bot using your chatbot code + custom tools
- Create a documentation assistant with system prompts for your product
- Add AI Elements to upgrade any existing chat interface
Ambitious Goals (1 week):
- Multi-step workflow automation with conditional tool chains
- RAG system combining extraction + search + synthesis
- Custom generative UI components for domain-specific displays
The gap between your code and production is just deployment. You already have the patterns.
generateText vs generateObject - Unstructured vs structured outputuseChat + streamText - The streaming chat duostepCountIs() - Enable multi-step conversations- AI Elements - Don't build UI from scratch
- System prompts - Control behavior and personality
- Tool schemas with Zod - Type-safe tool definitions
Most AI tutorials show you how to call an API. This course taught you:
- Why
generateObject beats generateText for real features
- How schema evolution works - start simple, add
.describe(), refine iteratively
- When to use Server Actions vs API routes for AI calls
- Why you experience the pain first - custom UI before Elements
- How multi-step changes everything - tool calls AND synthesis
- That debugging is part of the process - token counting, error handling, schema validation
The best AI features are invisible. Users shouldn't marvel at the AI - they should marvel at how much easier their work became. Focus on removing friction, not showcasing technology.
