Topic
AI Engineering
Shipping an AI feature is a full-stack engineering problem, not a prompt. Latency shapes the UX, security shapes what you are allowed to build, and the integration surface, meaning auth, permissions, and data access, routinely takes longer than the model work.
These articles are about that craft: keeping LLM products fast, using AI copilots well, defending against prompt injection when a system reads untrusted content and can also take actions, and answering the data-security questions a serious enterprise buyer will put to you before they sign.
What we would tell you on a call
Ship to production or it does not count
A demo proves the model can do it once. Production proves it survives the traffic, the edge cases and the second month. The engineering between those two points is most of the work and almost none of the marketing.
Small senior pods over large teams
Two to four engineers who own an engagement end to end move faster than a larger team with handoffs, and the people in the scoping conversation are the people who write the code.
Code in the client's repository from day one
Full IP transfer is easy to promise at the end and easy to prove from the start. It also forces the working practices, review and CI that make a handover uneventful.
Overlap hours beat total hours
Four guaranteed hours a day with the client's morning is worth more than a larger team on a different clock. The work that needs a decision gets one the same day.
Everything we have written on this
Prompt-Injection Defense Checklist
Prompt injection is the top security risk for production LLM apps and agents. Here is a concrete prompt injection defense checklist we use to harden real systems: threat classes, input and output validation, least-privilege tool scopes, grounding, human approval, and audit logging.
AI Copilots for Engineers Beyond Autocomplete
From 2022–24, AI coding tools moved from autocomplete novelties to opinionated copilots. The biggest wins came when teams designed them around real workflows, not demos.
Enterprise AI Data Security
A practical guide to enterprise AI data security for procurement, security, and IT leaders vetting an AI vendor. The questions to ask, the architecture that answers them, and the checklist to run before you sign.
Latency and UX in LLM Products
LLMs are slower than traditional APIs, but users are surprisingly tolerant when the UX is honest, responsive, and designed around perceived speed.
Proof in production
Key terms
- Prompt injection
- An attack where instructions hidden in content the model reads, such as a web page, document, or email, cause it to ignore its original task and follow the attacker instead.Any system where a model reads untrusted content and can also take actions needs to be designed against this from the start.
- Guardrails
- Checks applied to what goes into and comes out of a model: input validation, output schema enforcement, content filtering, and refusal handling.
- Observability
- Being able to see what an AI system did and why: the prompt, the retrieved context, the tool calls, the response, the latency, and the cost, for any individual request.When a user reports a bad answer, observability is the difference between diagnosing it and guessing.
- Structured output
- Constraining a model to return data in a defined shape, usually JSON matching a schema, so downstream code can consume it without parsing prose.Most integration bugs in LLM features come from free-text responses that code has to guess at.
The service behind this
AI-Powered App Development
Full-stack apps built faster with AI at every layer.
