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!
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.