Build DeepSearch in TypeScript
Build DeepSearch in TypeScript
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 46m | 666 MB
Instructor: Matt Pocock
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 46m | 666 MB
Instructor: Matt Pocock
Building AI applications that are genuinely useful involves more than just hitting an LLM API and getting back stock chat responses.
The difference between a proof-of-concept and a production application lies in the details.
Generic chat responses might work for demos, but professional applications need appropriate outputs that align with specific requirements.
In a professional environment code is (ideally) tested, metrics are collected, analytics are displayed somewhere.
AI development can follow these established patterns.
You will hit roadblocks when trying to:
- Implement essential backend infrastructure (databases, caching, auth) specifically for AI-driven applications.
- Debug and understand the "black box" of AI agent decisions, especially when multiple tools are involved.
- Ensure chat persistence, reliable routing, and real-time UI updates for a seamless user experience.
- Objectively measure AI performance moving beyond subjective "vibe checks" for improvements.
- Manage complex agent logic without creating brittle, monolithic prompts that are hard to maintain and optimize.
In this course you will build out a "DeepSearch" AI application from the ground up to help you understand and implement these patterns and ensure a production-ready product.
Build DeepSearch in TypeScript
