Build Advanced Q&A Bots for Production

Use Vellum to integrate your data, test and evaluate your prompt configuration, then easily manage once in deployment.

Screenshot of Vellum's playground

Develop Production-Grade

LLM-powered Q&A bot


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 accurate is the information extracted by LLMs using long-context window?

It depends on the model of use, but GPT-4 (128K) and Claude 2.1 (200K) have reported 90%+ retrieval accuracy. You can also utilize metadata filtering to improve the retrieval accuracy.

How are LLMs used in information extraction?

LLMs, when combined with Retrieval-Augmented Generation (RAG), can be used for information extraction by leveraging a two-step process where relevant documents are first retrieved from a database and then synthesized into concise information, enhancing the precision and relevance of extracted data.