Matt Pocock (AI hero) – Build Deep Search 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.

What Will I Learn?

• Understand the key differences between proof-of-concept AI apps and production-ready AI applications.

• Implement backend infrastructure tailored for AI-driven apps, including databases, caching, and authentication.

• Debug and interpret AI agent decision-making, even when using multiple tools.

• Build reliable chat persistence, routing, and real-time UI updates for a smooth user experience.

• Measure AI performance with objective metrics instead of relying on subjective impressions.

• Manage complex AI agent logic without creating brittle, hard-to-maintain prompts.

• Develop a fully functional “DeepSearch” AI application from scratch, following professional development patterns.

Target Audience

• Developers who want to move from AI prototypes to scalable, production-ready applications.

• Engineers and tech leads looking to integrate AI into real-world products with reliability and performance tracking.

• Backend and frontend developers who want to understand AI-specific infrastructure needs.

• Product managers or technical founders aiming to create polished AI tools beyond generic chatbots.

Requirements / Instructions

• Basic programming skills (JavaScript, Python, or similar).

• Understanding of web application development fundamentals.

• Willingness to work with both frontend and backend technologies.

• Interest in debugging and improving AI systems beyond surface-level outputs.

• Commitment to follow the course schedule as lessons unlock starting July 14th, 2025.