ChatGPT versus GPTBlue

What is better?

Sorry, there is no better. They are just different. Both use the same OpenAI large Language model.
The key question is: Do you prompt for yourself or do you create prompts for others in a business environment”?

ChatGPT

When you write prompts for yourself, you may prefer ChatGPT.
Why? it’s quick and you polish it until the results are good for you.

• Your prompt library is a long list accessible only to you.

GPTBlue

When you create prompts for others, you may prefer GPTBlue.
Why? It’s usually in a business environment, where you compose prompts for others.

• Productivity gain must be visible and measurable.
• Prompt Composition allows us to develop unique prompt features.
• Library networks are key in larger organizations. It’s part of GPTBlue today.
• Partner integration and productivity induction is becoming a strategic advantage.
• Analytics will help you and your users better understand the performance.
• Business Model allows prompt designers to sell the prompts with recurring revenue.
• Future Evolution looks like prompts will evolve into entire applications.

Learn more about this feature-rich solution on the GPTBlue web page.
If you develop prompts for other’s, try it out  it’s free

ChatGPT versus GPTBlue

It boils down to prompting for yourself or designing them for others as profession.

ChatGPT versus GPTBlue Architecture

GPTBlue is leveraging the Open AI Large Language Model (LLM) and its, Generative Pretrained Transformer (GPT). The value-add comes from an AI-Native application that sits on top of it. The prompt framing architecture frames the prompt functionality given by the LLM but allows additional prompt design features on top of it. The prompt composer

GPTBlue AI Network

Now, here GPTBlue goes really crazy. Based on the needs of some very large customers we developed a unique Gen AI Application Network. It started with the question how can we better deal with the rapid proliferation of prompts where nobody knows who has them, who wrote them and are they productive? A multi library network was a starting point. Rules for certain prompts to deploy to specific libraries only. The integration of business partners was next. It ended up with thousands of users, hundreds of libraries, central observation and decentral management. You will find more in our case study, available Beginning of March.

Because we are a highly productive AI company, we have far more time for our customers 🙂

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