Build DeepSearch in TypeScript

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.