Category archive
The architecture is what the product does when the model is wrong.
A model that is right most of the time is an architecture problem, not a prompt problem. These articles cover the surfaces that decide what the reader experiences on the other days: the contract on the output, the route to a second model, the trace that explains a bad answer, and the incident process for a system that is wrong fluently.
Articles in Product Architecture
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Incident Response for Systems That Can Be Wrong Fluently
A response playbook for quality regressions, prompt attacks, retrieval contamination, runaway agents, cost spikes, and provider failures.
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Observability for LLM and Agent Systems
Tracing model calls, retrieval, tools, state transitions, quality signals, and cost without turning telemetry into a privacy liability.
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Model Routing, Cascades, and Fallbacks
How to choose models per request using task risk, calibrated confidence, operational health, and total expected cost.
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Structured Outputs Beyond Valid JSON
Designing schemas, constrained decoding, semantic validators, repairs, and safe evolution for dependable model integrations.
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AI Product UX for Systems That Can Be Wrong
Designing intent, progress, evidence, editing, confirmation, and recovery so capability becomes trustworthy product behavior.
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Production AI Architecture in Laravel and PHP
Queues, streaming, typed provider boundaries, retrieval, tool execution, and observability for durable AI features in Laravel.
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Put this against a real system.
If a decision in Product Architecture is in front of you right now, the fastest version of this is a call: bring the architecture, the failure you are seeing, and the constraint you cannot move.
Discuss the system
