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.
.png)
.png)
Develop Production-Grade
LLM-powered Q&A bot
.png)
Search
Use proprietary data as context in your LLM calls.
Prompt Playground
Side-by-side prompt and model comparisons.
Workflows
Integrate business logic, data, APIs & dynamic prompts.
Evaluation
Find the best prompt/model mix across various scenarios.
Deployments
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.
.svg)
