AI project – ChatGPT API

Here I am going to explore how Chat GPT API can be leveraged to achieve a specific business aim.

In this case the fundamental question is how private and secure can interactions with chatGPT be for a business, for example one that deals with financial transactions and its data is a primary concern.

Firstly I’ve developed a standard ChatGPT API app here

However, to fullfill business objectives I need something MORE!

None of the options below fit the bill so I went with a different approach here

Below summary is taken as an answer from Chat GPT itself, when asked:

“I am using openai chatgpt api to host my own app that uses the LLM to summarise company knowledge. The chat bot is aimed to be an expert on the knowledge from all departments within the company. I know ChatGPT can be fine-tuned. I also heard of an offline way where data is not sent to openai. Can you outline all possible approaches to acheive this task and how I can vett the privacy of the data the model would be querying

Standard ChatGPT API Usage (covered in the first attempt here)

Approach: Utilize the standard ChatGPT API provided by OpenAI. This involves sending requests to OpenAI’s servers where the model is hosted.

Privacy Considerations:

  • Data Handling: All data sent to OpenAI’s servers is subject to OpenAI’s data policies.
  • Security Measures: Employ best practices for securing API keys and ensuring that only authorized queries are made.
  • Data Sensitivity: Be cautious about sending sensitive or proprietary information.

Fine-Tuning ChatGPT

Approach: Fine-tune the ChatGPT model on your company-specific data to make it an expert in your company’s knowledge. This involves training the model with a custom dataset that represents the knowledge from all departments.

Privacy Considerations:

  • Data Sharing: Fine-tuning requires sharing your dataset with OpenAI for the training process.
  • Control Over Data: Ensure the dataset doesn’t contain sensitive or confidential information.

Deploying an Offline Version (if available)

Approach: If OpenAI offers an offline version of their model, you can deploy it within your own infrastructure. This would involve running the model on your servers without sending data to OpenAI.

Privacy Considerations:

  • Data Control: Full control over your data since it doesn’t leave your infrastructure.
  • Infrastructure Security: Ensure that your internal servers and infrastructure are secure.

Using Open-Source Alternatives

Approach: Utilize open-source models similar to ChatGPT, like GPT-Neo or GPT-J. These can be hosted on your servers.

Privacy Considerations:

  • Data Control: Like the offline version, you have full control over your data.
  • Model Training: You might need to train or fine-tune the model on your specific dataset.

All of the above options, however are not feasible. The online versions share too much information with third parties. the offline version would be too costly, unless clear profits from its implementation were first assessed.