How do companies actually use LLMs in production?
We updated our curated list of ML use cases (300+ case studies from 80 companies 🔥), including a bunch of new use cases on LLMs.
Here are some interesting examples we found:
🔠 Grab uses LLMs for data governance. They automatically classify data entities, such as whether data contains PII or other sensitive information.
🛍 Whatnot improves marketplace spam detection. They use LLMs to identify potentially fraudulent messages as an additional signal for their rule engine.
🏠 Nextdoor personalizes customer communications. They use LLMs to come up with interesting subject lines for emails and notifications
🧠 Instacart built an internal AI assistant called Ava. It helps in many internal processes, from code review and debugging to Slack message summarization.
🍿 Vimeo built a customer support AI assistant. Users can chat with a bot and get personalized responses based on the help center data.
I wish more companies shared pragmatic, real-life examples like this! Did we miss some great use cases? Share the links so we can add them to the next update of our database!
Executive Summary
We’ve never seen a technology adopted as fast as generative AI — it’s hard to believe that ChatGPT is barely a year old. As of November 2023:
Two-thirds (67%) of our survey respondents report that their companies are using generative AI.
AI users say that AI programming (66%) and data analysis (59%) are the most needed skills.
Many AI adopters are still in the early stages. 26% have been working with AI for under a year. But 18% already have applications in production.
Difficulty finding appropriate use cases is the biggest bar to adoption for both users and nonusers.
16% of respondents working with AI are using open source models.
Unexpected outcomes, security, safety, fairness and bias, and privacy are the biggest risks for which adopters are testing.
54% of AI users expect AI’s biggest benefit will be greater productivity. Only 4% pointed to lower head counts.
