Why One Model Should Not Handle Every AI Task
Using one model for every AI task turns model choice into a hidden default. Production workflows need explicit model policies for task type, risk, latency, evidence, fallback, and evaluation.
Topic
Building AI systems
Architecture that keeps the model as one step, not the whole system.
Code-First AI Systems: Why the Model Should Not Run the Workflow
The model should classify, extract, summarize, judge, or propose. Code should own routing, state, validation, retries, evidence, review, and evaluation.
Pydantic as an AI Architecture Boundary
LLM output is not application state until it passes a typed boundary. Schemas, validation errors, evidence fields, and versioned contracts make AI workflows easier to inspect and change.
The Death of the 1,000-Line Prompt
Giant prompts feel productive at demo stage. In production, workflow logic needs to move into code, schemas, routing, validation, evidence, review, and evaluation.