[ Level 03 ]
System Architecture
API integrations, context management, tools, error handling, safety, costs, and security.
API integrations: calling the model from your program
How your program calls an AI model directly through the API: requests, responses, API keys, rate limits, and testing the first call before production.
Updated 2026-10-05
Context management: how the model “remembers”
Why AI models have no memory, how conversation history grows, and three strategies — a limited window, summaries, and a state of affairs — for managing context.
Updated 2026-10-05
Tools and actions: let the model act
How function calling works: the model decides, your system acts. Writing tool descriptions, the flow from question to answer, and when a workflow step is enough.
Updated 2026-10-05
Errors and error handling
Five common error types in AI workflows and how to handle them: retries with limits, planned fallbacks, logging, and when to alert a human.
Updated 2026-10-05
Safety: limits and human-in-the-loop
Why independence and damage grow together, and the four safeguards — permissions, approval, a kill switch, an audit trail — that keep the human in the loop.
Updated 2026-10-05
Cost management: tokens, prices, budget
Where AI costs come from — input, output, and growing history — plus a simple estimating formula, monitoring habits, five ways to cut spend, and budget limits.
Updated 2026-10-05
Security: keys, data, prompt injection
The three security risks of AI systems — leaked API keys, over-shared data, and prompt injection — and the layered technical defenses that keep them contained.
Updated 2026-10-05