Generative AI and LLMs

This hub tracks how generative AI and large language models move from demo to production, along with the failure modes and design trade-offs that only surface once real traffic hits the system. We cover how large language models work in plain terms, aimed at teams that need to explain the technology to a board without a whiteboard full of transformer diagrams.

Not every generative AI project survives contact with production traffic, and we write about that too. One article walks through common pitfalls in AI-generated code that surface in code review, from silent logic errors to security gaps that linting will not catch. Another looks at agentic systems reshaping operations, where autonomous agents start making decisions instead of just answering prompts, and what that means for testing and rollback plans.

Our teams have built retrieval pipelines, agent orchestration layers, and fine-tuned models for clients who needed something that worked past the prototype stage, work we do as part of our AI software development practice. If you are scoping a generative AI build and want engineers who have already hit these problems, our AI development services team can walk through what a production rollout actually requires.

If you are new here, start with the piece on how large language models work, since it grounds the rest of the hub. It helps you decide whether your team needs a simple integration or a full agentic system before committing engineering time.