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.
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Tag
Evaluation
Token Economics: Why Prompt Bloat Kills AI Margins
Token waste is architecture debt. Oversized prompts, broad context, verbose outputs, retries, review, and eval runs compound into the cost per trusted completed unit.
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How to Trace a Failed LLM Run
When an AI workflow fails, the final answer is not enough. You need the input, route, prompt, model, context, schema errors, evidence gaps, review action, and eval case.
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What Happens the Day After an AI System Launches?
Launch is not the finish line for an AI system. It is when review queues, failures, costs, user feedback, model changes, and ownership finally become real.
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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.
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Evaluating PolicyTrace: Golden Examples, Conflicts, and Review Outcomes
How I would evaluate PolicyTrace beyond simple accuracy: golden examples, conflict fixtures, provenance checks, review outcomes, regression gates, and operational metrics.
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What I Would Improve Before Taking PolicyTrace to Production
A production hardening roadmap for PolicyTrace: authentication, durable storage, background jobs, audit trails, evaluation, monitoring, retention, and operational ownership.
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Evaluation Is an Engineering Problem
Why AI evaluation is not a report card after launch, but a design constraint from day one.
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AI Systems Need Evidence, Not Just Outputs
A practical framework for making AI outputs reviewable, traceable, and trustworthy in real workflows.
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