Matt Pocock (AIhero) – Build DeepSearch

Build DeepSearch by Matt Pocock is a practical course about taking AI applications beyond a quick demonstration and preparing them for real users. The course project examines the backend infrastructure, testing patterns and debugging techniques involved in developing a production-oriented system.

The downloadable materials include videos and documents. The project uses TypeScript and focuses on the engineering challenges that arise around an AI feature, not just the initial API call.

Skills you’ll build in Build DeepSearch

• Learn to build robust backend infrastructure that scales effectively.
• Implement proven debugging techniques to troubleshoot AI systems.
• Design maintainable agent logic for easier updates and modifications.
• Measure performance accurately to enhance AI application efficiency.
• Utilize TypeScript for improved type safety and reliability.
• Collaborate with peers to overcome common development challenges.

From AI demos to production systems

A simple AI prototype can return a response from an API without addressing what happens when an application has ongoing users, accumulated state or unexpected agent behavior. Build DeepSearch takes those surrounding engineering problems as part of the project.

The course looks at situations such as an agent producing strange responses or hallucinating, a chat application failing after a long conversation, and errors that are hard to diagnose. These examples frame the need to understand what an AI system is doing rather than treating its output as a black box.

The project is intended to reflect the challenges of software that could be shipped to customers. Backend design, testing and systems built for real traffic are part of that work alongside the AI functionality itself.

TypeScript and common AI development challenges

Build DeepSearch uses TypeScript throughout the project. Its type-safety approach is applied to AI application code to help catch errors and make the system easier to work with as it develops.

The course addresses common challenges including agents making unexpected decisions, managing application state and handling errors. It connects these AI-specific issues with software engineering practices rather than treating the model response as the whole application.

What the Build DeepSearch course covers

The infrastructure work includes database design, caching and authentication. These backend components support the application around the AI feature and are considered alongside the demands of real users.

Application architecture topics include chat persistence, routing and real-time updates. The course examines these pieces in the context of a system expected to handle traffic rather than a demonstration used only once.

For AI agents, the course covers tools and techniques for understanding their behavior, as well as ways to structure prompts and logic for later changes. It also addresses objective performance measurement, giving developers a way to assess an AI system rather than relying on guesswork.

Testing is another part of the project. The course presents testing strategies for AI systems together with debugging approaches for investigating failures, so that infrastructure and agent behavior can be examined as the application evolves.

About Matt Pocock and Build DeepSearch

Matt Pocock has spent years building complex TypeScript applications. Build DeepSearch brings that experience to AI development, combining TypeScript practices with attention to how AI systems behave outside a small prototype.

His course takes a practical approach to technical problems, including confusing errors and behavior that changes as an application is used. The emphasis is on understanding the infrastructure and code behind an AI feature.

Who the course is for

Build DeepSearch is intended for TypeScript developers who want to add AI capabilities while retaining established software engineering practices. Backend engineers can explore AI alongside the infrastructure and testing concepts used to keep applications running.

It also suits developers who have made AI prototypes but encountered problems moving them toward production, as well as teams integrating AI into existing applications. The course assumes programming knowledge, including TypeScript and basic familiarity with databases and APIs; it does not require learners to be AI experts.

Frequently asked questions

What does Matt Pocock’s Build DeepSearch course cover?

Build DeepSearch covers the infrastructure, testing and debugging involved in production-oriented AI development. Topics include databases, caching, authentication, chat persistence, routing, real-time updates and AI agent patterns.

Do I need to be an AI expert before taking Build DeepSearch?

No prior AI expertise is required. The course assumes you can code in TypeScript and understand basic backend concepts such as databases and APIs.

Who is Build DeepSearch designed for?

The course is suited to TypeScript developers, backend engineers and developers moving AI prototypes toward production. Its approach is also relevant to teams adding AI features to existing applications.

What course materials are included?

The downloadable materials include videos and documents. The course centers on a TypeScript project that explores the practical infrastructure around an AI application.

Does the course focus on a particular AI model?

The course emphasizes fundamental infrastructure patterns rather than a specific AI model. It also covers ways to structure agent prompts and logic so they can be maintained.