Hire an OpenAI and LLM developer

What the work looks like

A large language modeli, or LLM, is called by your app through an API. The real work is around it: what you send, what comes back, how you keep answers grounded in your own content, and what happens when the model is wrong or slow.

• Assistants that answer questions about your product or documents.
• Writing helpers, translators and summarisers inside a workflow.
• Tools that read documents or images and pull out the data.
• Automations that decide, sort or draft replies.

These are the building blocks of an AI product. The developer usually works with a backend in Python or Node.js and a front end in Next.js or a mobile tool. Other AI providers exist, and a good developer will tell you if another model suits your case better.

What to ask an LLM developer

• How will we measure whether the answers are good enough? Can we put together a set of test questions?
• How do you stop the model from making things up about my content?
• How do you keep my private data out of places it should not go?
• What happens when the service is slow, down or returns something unexpected?
• How do you track and limit usage so the bill stays predictable?
• How do you keep the keys safe, and can we switch the model later?

AI answers are never perfect. Plan for review steps where a person checks important output, and let the developer show how mistakes are handled.

A sensible order of milestones

Start small and test with real examples before building screens. Each milestone is funded before work starts and runs 1, 2, 3, 4, 6 or 8 weeks. The weeks in this example are for illustration, and developers set the length of each milestone in their proposal. Milestones are from 50 to 2,000 each in your project currency.

1. Prototype (for example, weeks 1 to 2): one core task works with your sample content, results shown in a simple page.
2. Quality set (for example, weeks 3 to 4): test questions, scoring of answers, first round of tuning.
3. Product (for example, weeks 5 to 7): logins, saving history, usage limits, error handling, a clean interface.
4. Launch (for example, week 8): monitoring, cost alerts, privacy notes, handover.

Matching for AI work

Developers can list OpenAI under Expert ini or under Also works withi. Your project is scored on this, on help type and project type, and on portfolio, budget fit and availability.

The score is 0 to 100 and comes with plain reasons. At least 40 is needed to apply. 80 or more earns a Top match badge. Proposals reach you sorted by score.