Patterns is a unified data processing platform that is purpose built for connecting state-of-the-art AI models to your business systems. Patterns does this by providing an open platform for developing, deploying and sharing integrations and applications that run on a standard protocol. With technology constantly advancing, many AI tools are only available to those who can afford them. By offering access to the latest and greatest models and a platform for sharing AI applications, we want to enable more people to work with AI.

If you have data,  build an automated slack bot that would respond to tech support questions with knowledge from our technical documentation, community slack channels, and previous support ticket would be doable, then the problem we’re working on at Patterns is how to scale our technical support. 

The immediate problem I encounter now is that we have far too much content to fit in the 2048 token limit for the normal prompt though. LLM providers like OpenAI provide a “fine-tuning” api where you can submit labeled example completions to fine-tune your own version of their LLM, it could take our rich text corpus, fine-tune a model, and then provide completions via a Slack bot.

There are many ways to import our documentation into Patterns. To keep it simple for our example, we loaded ours into an Airtable base that we imported via the standard Airtable component in Patterns.

Upload training data to OpenAI, and start the fine-tuning job​

This resulted in prompt/completion pairs that we could structure and upload to OpenAI for fine-tuning. This involved first uploading our completions, which took a few seconds, and then kicking off the fine-tuning job on OpenAI.

Depending on the size of training data, and OpenAI’s resource availability, fine-tuning can take anywhere from a few minutes to a few hours, for that reason we kept a state object in our fine-tuning script and check for updates until it’s finished.

other cases to reference are:

• What Happened This Week? Create a Slack Bot that Summarizes Channels with GPT-3
• Tapping into the knowledge of Reddit with LLMs
• Using Patterns and Cohere to classify Salesforce cases with a Large Language Model (LLM)
• Replacing a SQL analyst with 26 recursive GPT prompts
• Prompt engineering davinci-003 on our own docs for automated support (Part I)

In summary, small teams/firms are possible to take down financial info industry behemoths such as Bloomberg by leveraging AI. As of now, aided by Patterns to orchestrate data and Co:here to fine-tune/train model on specialized domain.