Building an AI Assistant: From First Workflow to Production

An AI assistant should start with a specific job, not a model choice. Will it answer support questions, qualify leads, or help staff find internal information? Write down the workflow, the people who use it, the data it needs, and the cost of a wrong answer. This defines the smallest useful first release and gives the project a way to measure success.

Next, inspect the knowledge sources. Product data, policies, and documents need an owner, an update process, and access rules. A small, stable knowledge set may need only targeted API calls or search. Retrieval-augmented generation (RAG) helps when answers must draw from a larger, changing collection. It can find relevant passages, but it cannot guarantee that a model interprets them correctly.

Write the system prompt as a behavior contract: the assistant’s role, allowed tasks, approved sources, boundaries, tone, and when to ask a follow-up or hand the conversation to a person. Include examples of ordinary and difficult requests. Define what to do when evidence is missing or contradictory. A prompt guides the model; permissions and validation still belong in application code.

Connect only the tools that complete the chosen workflow, such as catalog search, appointment availability, or a CRM draft. Separate read actions from write actions, check authorization on the server, and require confirmation before consequential changes. Start with one bounded workflow. Giving an agent broad tools before its permissions and failure paths are clear creates avoidable risk.

Build an evaluation set before launch from real or carefully representative requests: common questions, ambiguous wording, missing evidence, unsupported requests, and attempts to override instructions. Check factual grounding, correct tool use, follow-up decisions, handoffs, latency, and cost. Re-run the same cases after changing the prompt, retrieval settings, or model. Fluent answers alone are not evidence of reliability.

Release to a small group, inspect failures, and then expand. In production, track whether users finish the task, correct the assistant, or ask for a human. Keep enough logs to diagnose problems while minimizing personal data. Assign ownership for updating knowledge, handling provider outages, and rolling back a bad change. Ongoing operations are part of the assistant, not work to postpone until after launch.

If you want an AI assistant designed and built around your workflow, send me the task and its main constraint.