Nicolay Gerold

The idea is: learn to prompt chatbots very well = get way better outputs.

Right now, intricate prompting is helpful for some tasks. But over time, we think it’s an overrated skill. Here’s why:

1. As AI models improve, they require less “engineered” prompts. DALL-E 3 is a great example of this (you get top-tier images with < 10-word prompts).

In this case, it will still... See more

Start with an ambitious, high-potential idea, but challenging and full of ambiguities — the rough shape of a nascent great idea. It typically isn’t new, but solves many problems in a simpler, more intuitive way. It ignites people’s curiosity and self-motivation. At the end, it should seem “obvious” so that others want to copy, because there aren’t... See more

Higher performance and lower cost than any single LLM

We invented the first LLM router. By dynamically routing between multiple models, Martian can beat GPT-4 on performance, reduce costs by 20%-97%, and simplify the process of using AI

Document search and synthesis

Scores of organizations want to harness generative AI so employees can easily find the most relevant documents through improved search results and summaries. For example, your organization can reduce the time it takes employees to find answers to common HR- and process-related questions. Internal manuals and sites are... See more

The xAI PromptIDE is an integrated development environment for prompt engineering and interpretability research. It accelerates prompt engineering through an SDK that allows implementing complex prompting techniques and rich analytics that visualize the network's outputs. We use it heavily in our continuous development of Grok.

HoneyHive is a collaboration platform to test and evaluate, monitor and debug your LLM apps, from prototype to production. It enables you to continuously improve LLM apps in production with human feedback, quantitative rigour and safety best-practices.

Watching intense light between 10 and 4 at Night reduces the capacity to produce dopamine.

You have this classic issue where most researchers are evaluat[ing] against fixed data sets [. . . but] most industry methods change their datasets. We found that these dynamic validation sets served two purposes: (1) the obvious goal of making sure the validation set stays current with live data as much as possible, given new knowledge about the... See more

RedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text

documents coming from 84 CommonCrawl snapshots and processed using

the CCNet pipeline. Out of these, there are 30B documents in the corpus

that additionally come with quality signals. In addition, we also provide the ids of duplicated documents... See more