Prompt Injection Is a Data Problem: A Threat Model You Can Ship Against
Prompt injection can't be filtered away: the model can't reliably tell instructions from data. Here's the actual threat model and the controls that hold up.
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Notes from the workbench.
Working notes on shipping AI in production: what breaks between demo and deployment, what's worth buying instead of building, and how we test the parts that matter.
Why Your AI Agent Works in the Demo and Stalls in Production
An agent that nails the demo stalls in production because reliability compounds across steps. Here's the math, the real failure modes, and how to ship one anyway.
Read →What AI Actually Costs in Production, by Workload
A modeled, reproducible benchmark of cost-per-successful-outcome across four common AI workloads, with every token assumption, price, and overhead multiplier shown.
Read →Model-Agnostic by Design
Models change under you every few months: price, quality, and capability. Here's why we never hardcode a single provider into a client's feature.
Read →Your RAG Demo Lied to You
Retrieval that looks flawless on ten clean PDFs falls apart on a real corpus. Here's why, and what evaluating retrieval quality actually requires.
Read →What an AI Feature Actually Costs in Production
Token costs that look trivial in a demo compound fast at scale. Here's how to make cost a first-class metric instead of a surprise on the invoice.
Read →Evals Before You Ship: Why AI Features Need Tests Too
Shipping an AI feature without an eval suite in CI means every prompt tweak is a guess. Here's what an eval suite actually needs to cover.
Read →Build, Buy, or Skip: A Framework for AI Decisions
A technically honest framework for deciding whether an AI initiative should be built in-house, bought off the shelf, or skipped entirely this year.
Read →From Notebook to Production: What Actually Breaks
The gap between a working AI demo and a production feature is auth, latency, cost and fallbacks. Here's how we close it without a rewrite.
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