Matt Pocock (AIhero) – Build DeepSearch in TypeScript

This is a cohort-based course and the lessons will start unlocking on July 14th, 2025.

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.