Prompt tuning can improve a demo, but it cannot carry a product. Production LLM applications need clear boundaries: what context enters the model, how tools are invoked, what happens when retrieval fails, and how operators observe behaviour in the field.
Architecture decisions that determine adoption
Chunking strategy, citation requirements, latency budgets, and human review points determine whether an LLM feature is adoptable or abandoned after the pilot.
Teams that invest in observability early (traces, eval sets, failure taxonomy) learn faster than teams that iterate on wording alone. The goal is reliable behaviour across changing data and users, not a single impressive transcript.
A practical engineering stack
Microcorem treats LLM applications as engineered systems: retrieval, orchestration, evaluation, and rollout discipline together — with prompts as one layer, not the whole stack.