AI Application Development: From Idea to Production
Doris Infotech
From idea to production, AI application development looks like product engineering with extra failure modes. Hallucinations, prompt injection, token cost, and silent quality drift do not show up in a weekend prototype. They show up when real users ask messy questions.
At Doris Infotech we treat the model as a service behind a contract: input, output schema, latency budget, and a fallback. The application around it - auth, logging, retrieval, human review - is the product. Skipping that layer is how pilots never leave the lab.
The path is not “pick a model, wrap a chat UI.” It is problem, data, evaluation, then a release you can observe and roll back.
Scope the job before you pick the model
Classify, extract, generate, recommend, or converse - those are different products. Write the user job, the allowed actions, and what must never happen. If a wrong answer can move money or medical advice, the architecture changes. A foundation model is a tool. The job decides whether you need retrieval, fine-tuning, a smaller classifier, or no model at all.
Ground the model in your data
Most business AI apps need retrieval: documents, tickets, product catalog, policies. Chunking, permissions, and freshness matter more than a clever system prompt. If the model cannot cite a source the user can open, it is guessing. Build the index and access control first. Then generate.
Evaluate like you mean to ship
Keep a set of real examples: gold answers, edge cases, jailbreaks, empty input. Score accuracy, refusal, and latency on every change. A vibe check in Slack is not QA. When quality drops after a model update, you need a number and a rollback, not a surprise in production.
Wrap it in software you can operate
API gateway, auth, rate limits, tracing, cost per request, and a UI that shows loading and uncertainty. Log prompts and outputs with privacy rules. Version the prompt and the retrieval config like code. An AI feature without observability is a black box you cannot support at 2 a.m.
Release with a human path and a cost cap
Start with a cohort, a review queue for low-confidence results, and a budget alert. Production is a loop: user feedback, failed queries, new documents. Idea-to-production is done when the team can change the app without a research sprint. That is application development. The model is just the part that infers.
