building LLM applications

An LLM by itself is just a text-in, text-out engine; a product is everything you build around that engine so a real user gets reliable value. Think of the model as a powerful but unpredictable employee — building an application means giving it a job description (the prompt), the right reference material (retrieval), tools it can use, checks on its work, and a clean interface for the person who actually needs the answer.

In practice an LLM feature is a pipeline: you receive a user request, assemble a prompt from templates and retrieved context, call the model — often more than once — validate and parse what comes back, perhaps call tools or a database, and render a result. Around that loop sits the unglamorous engineering — error handling, logging, cost controls, evaluation, and safety — that turns a clever demo into something dependable enough to put in front of customers.

The hard part is rarely the model call itself; it is the surrounding system. Demos are easy and production is hard because language is open-ended: users phrase things you never anticipated, the model occasionally returns nonsense, and small prompt changes ripple through quality. Treating LLM work as ordinary software engineering — with tests, monitoring, and graceful failure — is what separates a viral toy from a product people trust.