Coming soon: Humanloop for LLMs

By Jordan BurgessCofounder and CPO

Today, we are announcing the preview of Humanloop for Large Language Models (LLMs).

LLMs like GPT-3 are a powerful and disruptive technology. With GPT-3, developers can integrate language-understanding AI in their applications with only a couple lines of code and some creative prompting. You can see impressive use cases of copy writing, intelligent summarisation, and even things like English to Regex and jargon to English.

However, there are many challenges between impressive demos and practical real-world applications. Off-the-shelf GPT-3 models can struggle with more specialized tasks that require domain knowledge or a specific style. Additionally, these models have reliability issues (there’s no guarantee it won’t generate incorrect or problematic outputs in response to certain inputs) and they can be too slow and expensive for certain use cases at scale.

Humanloop is helping tackle these issues. We’re currently helping companies that are building with GPT-3 create a systematic way to evaluate and improve their models. This makes them cheaper, faster and more reliable, while also providing you with a way to make your data a competitive advantage.

If you’re building with GPT-3 or other large language model, sign up to be a part of our closed beta.

Please fill out this quick form or email us at llm@humanloop.com for access.

Frequently asked questions

What is Humanloop?

Humanloop is a platform for rapidly building and improving NLP models using a cutting-edge active learning approach.

What are LLMs (Large Language Models)?

Large Language Models are machine learning models that are large (many billions of parameters) that have been trained to predict the patterns in language. They have shown remarkable capabilities for a wide range of tasks like summarisation, question answering or even writing code or poetry. Typically they'll have been trained on petabytes of text data (the internet and other sources) to predict the next word in a sentence.