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AI Application Architecture

Follow the parts of an AI app, from a user’s question to an answer backed by relevant information.

About This Course

Introduction

An AI app connects a task with data, models, tools, and operating controls. The model contributes prediction or generation. Authentication, current information, memory, validation, payments, and recovery still need to be designed around it. We will build that architecture by following one request at a time.

The route covers APIs, clients and servers, context construction, structured output, retrieval, routing, caching, memory, observability, and deployment. The project is an interview-intelligence system using a résumé, story bank, company material, and job descriptions. Its purpose is truthful preparation and practice; inventing experience or submitting applications outside an authorized workflow would undermine that purpose.

Use Modules 0, 13, 14, 16, and 19 as reference points. Begin with requests and context, then retrieval and responses, then cost, state, and operations. We will explain components through the problems they solve. You can understand the architecture without committing to a particular software framework.

Planned Lessons

  1. Frontend, Backend, And The Request PathFollow a question from the screen through an app’s services and back to the person asking.
  2. APIs, HTTP, And JSON PacketsRead the messages apps use to ask other services for information.
  3. Authentication, Authorization, And SecretsSeparate checking who someone is from deciding what they can access.
  4. Context Engineering And The Context BuilderChoose the instructions and supporting information an AI model needs for a request.
  5. Structured Outputs And ValidationCheck whether an AI response follows the format and rules an app expects.
  6. Retrieval-Augmented Generation, Step By StepFollow an app as it finds relevant information and gives it to a model before answering.
  7. Search Quality, Hybrid Retrieval, And RerankingCompare ways to find useful information and put the best matches first.
  8. Grounding And CitationsCheck whether the sources attached to an AI answer support what it says.
  9. Routing, Fallbacks, And Graceful FailurePlan how an app should respond when a model or service fails.
  10. Caching And InvalidationSee when an app can reuse an earlier result and when that result is out of date.
  11. State And MemoryDecide which information an app should keep between questions or visits.
  12. Tool Calls, Timeouts, Retries, And IdempotencyHandle failed and repeated tool requests without accidentally doing the same action twice.
  13. Latency, Concurrency, And Cost BudgetsShare the time and cost allowed for a request across the services doing the work.
  14. Observability And DebuggingUse an app’s records to find the cause of a wrong, slow or failed response.
  15. Frameworks, Deployment, And Change ControlPlan how changes to an AI app are checked and released.