LLM Integration
Large language models wired into real product workflows
What It Is
LLM integration is the work of connecting a large language model the kind that powers chat assistants and text generation into an actual product, so it reads real data, respects real permissions, and produces output a downstream system or user can act on. That's a different job from calling an API and printing the response; it means prompt design, context management, and handling a model that can be confidently wrong.
Why It Matters
An LLM integrated without engineering discipline becomes a liability fast: it can be prompt-injected, it can hallucinate a fact a user then acts on, and it can leak context it was never supposed to see. Treating an LLM like any other untrusted external dependency validated inputs, bounded outputs, monitored calls is what separates a demo from a product feature.
How TieTech Uses It
We integrate LLMs with input validation at every model boundary, rate-limited and monitored inference endpoints, and human-reviewable fallbacks for low-confidence outputs the same approach documented in our AI agents and production AI case studies. MicrovestApp's Drippy Droppy assistant is a live example: an LLM-backed feature reading market conditions and returning savings guidance a non-trader can act on.
