Build Custom LLM Chatbot for Production

Build a reliable chatbot with Vellum's advanced tooling for prompt testing, evaluations and management once in production.

Screenshot of Vellum's playground

Develop Production-Grade



Use proprietary data as context in your LLM calls.

Prompt Playground

Side-by-side prompt and model comparisons.


Integrate business logic, data, APIs & dynamic prompts.


Find the best prompt/model mix across various scenarios.


Track, debug and monitor production requests.

Frequently Asked Questions.

How to test an AI chatbot?

To evaluate the responses of your AI chatbot, you can use developer platforms like Vellum to simulate conversations and evaluate the responses using ground data or LLMs. Once in production, you can further optimize and improve performance by testing the workflow with real user data.

How to make an AI chatbot sound more human?

To make an AI chatbot sound more human, make sure to invest some time to engineer a prompt that will give the best answer, incorporate tone and continuously test and improve with ground data or real users. Additionally, ensure there's an option for escalating to human customer support for queries the chatbot can't adequately address.

How to build a chatbot from scratch?

To build a chatbot from scratch follow these steps:

  1. Define the purpose and scope of your chatbot;
  2. Define the various intents users might;
  3. Choose a framework or a AI development product like Vellum;
  4. Design the conversation flow and user interactions;
  5. Test the prototype with a bank of test cases to see how it performs;
  6. Deploy to production, and monitor how users interact with it; and
  7. Continuously monitor and update the chatbot for improvements.