Labs Report 33: GPT and Large Language Models
Reading between the lines of the artificial intelligence hype.
The success behind ChatGPT demonstrates the power of Large Language Models to generate content, automate processes, simulate characters and much more. These models, which can create sentences that closely resemble human speech and writing, are changing how we create, adapt or draw conclusions from written text.
In this report, we’ll cover everything you need to know about LLMs: what the competitive landscape looks like, how these models can be applied to your work, what changes with the release of GPT-4, and considerations for responsible use. After taking stock of the present, we’ll also look at what the future of text might look like as LLMs become more sophisticated and tailored for specific uses across industries. In addition, we’ll share our prototype built using GPT and Slack’s API that’s designed to jumpstart brainstorms among a group of collaborators.
In this report, you will learn:
- Why LLMs are so effective
- How GPT-4 changes the game
- Ways to apply this technology now
- Important considerations to keep in mind
- The upcoming impact of LLMs and how to prepare
LLMs can be quite powerful, as we're seeing with GPT-4’s latest release. It’s going to help us work smarter, instead of harder if used responsibly.
Brian: A creative co-collaborator brainstorm bot powered by chatGPT.
To make GPT more accessible across our team and to integrate it within commonly used workflows, we created a chatbot that integrates into our widely used communication tool, Slack. Unlike typical chatbots that only engage in one-on-one conversations with users, Brian is designed to actively contribute and facilitate brainstorming sessions among teams.
Ultimately, the chatbot streamlines team brainstorming and collaboration within the Slack platform by guiding the conversation, sharing its own ideas, summarizing the discussion and more.
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