Blog
News, guides, and updates from Curtis Foster.
Prompt engineering is table stakes. System-prompt engineering is the real skill
Prompt engineering can make a demo work. System-prompt engineering makes an LLM product behave reliably when users, tools, and edge cases arrive.
A founder’s guide to avoiding AI vendor lock-in without slowing down
You do not need to build every AI component yourself. You need a small model abstraction, clean boundaries, and a plan for switching before switching becomes urgent.
How I ship a new AI product in a weekend and the three things I refuse to skip
A practical weekend process for building an AI MVP without confusing speed with recklessness. Ship fast, learn in public, and keep the safeguards that protect users.
AI guardrails aren't a filter. They're an architecture
A production AI product needs controls around data, tools, models, users, and recovery paths. One moderation filter cannot carry that load.
Multi-model orchestration: why I stopped betting a whole product on one model
A production AI product should not depend on one model's price, latency, or behavior. Here is how I use routing, fallbacks, and evaluation to spread that risk.
The real cost of an AI feature: instrument AI cost before it surprises you
An AI feature costs more than its model call. Learn how to measure retries, tokens, latency, storage, and AI unit economics before your bill gets ahead of you.
What long-term memory actually takes in a production LLM application
LLM memory is not a bigger context window. It is a data system with rules for capture, retrieval, correction, privacy, and cost.
RAG is a data-quality problem, not a search problem
Most RAG failures begin before vector search runs. Fix the source data, ownership, and update process, and retrieval becomes far easier to trust.
The eval loop is the product: why I build evaluations before features in any AI app
AI evals are not a final QA step. They are how you decide what to build, what to fix, and whether an AI product deserves user trust.
Why your AI demo works and your AI product does not
The last 20% of an AI product is where reliability, evaluation, cost, and user trust get decided. Here is how to close the gap.
Subscribe to Curtis Foster
Get new posts in your inbox. No spam, unsubscribe anytime.