AI Application Architecture: Core Concepts for AI Engineering

Build the mental model

AI Application Architecture focuses on this learner need: Build an AI feature with explicit inputs, model/provider boundaries, structured output or retrieval context, repeatable evaluation cases, and operational limits. Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery.

Track the changing state and identify the evidence that makes that state observable.

Identify the parts and boundaries

In AI Application Architecture, input/context contract. Model/provider abstraction. Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery.

1. 1

   Input/context contract.

2. 2

   Model/provider abstraction.

3. 3

   Structured output or retrieval grounding.

4. 4

   Evaluation, safety, latency, and cost.

Trace one concrete case

Choose one realistic input for AI Application Architecture and trace it using this path lens: Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery. Predict the result before running the example, then compare prediction with evidence.

If the prediction fails, identify the assumption before changing the implementation.

AI APPLICATION ARCHITECTURE
===========================
1. Input/context contract.
2. Model/provider abstraction.
3. Structured output or retrieval grounding.
4. Evaluation, safety, latency, and cost.
Evidence: the relevant output, test, log, query result, or rendered state for AI Application Architecture

Read the concept map, predict one concrete result, then compare that prediction with the module example or native tool.

A module-specific concept trace connecting core decisions to observable evidence.

practice/\n├── README.md\n├── ai-application-architecture-concept-map.txt\n└── evidence/\n    └── expected-result.txt

Compare a nearby alternative

For AI Application Architecture, compare the shown mechanism with a nearby alternative. Use this technical point—Structured output or retrieval grounding.—inside this path context: Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery.

State the tradeoff in your own words.

Explain it back with evidence

Summarize AI Application Architecture without reading the example. Explain the input or state, operation or decision, and result through this implementation lens: Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery.

For AI Application Architecture, use this evidence standard: the relevant output, test, log, query result, or rendered state for AI Application Architecture. Interpret the evidence through this path context: Use model/provider interfaces, prompts and context windows, retrieval, tool calls, structured outputs, evaluation datasets, safety/privacy controls, latency/cost telemetry, and failure recovery.

Common mistakes to avoid

• Prompt changes not versioned.
• Retrieval returns irrelevant context.
• Structured output not validated.
• No test set for regressions.