Prompt Engineering Workflow
Getting the best answers from ChatGPT and other LLMs is an art and needs multiple iterations for each prompt. EnterGPT hosts your prompt text and dynamic variables in the cloud instead of your developers code base. An integrated AI copilot helps to finetune your prompts.
Enabling your preferred LLM whisperers to finetune your prompt engineering and test out different versions.
Custom Data Integration
Foundation models like ChatGPT or LLama2 are very, very powerful and have a huge knowledge base. But they don't know anything about your custom data that you might want to apply and combine with the intelligence of those foundation models. The method to do this is called RAG (Retrieval augmented generation) and it takes some effort to realize it.
With EnterGPT you can easily apply your own custom data to the RAG process.
When working with LLMs, it is not straight forward to ensure a structured json result that you can use to structurally integrate result data into your apps. You need to handle unparsable data, prompt size limitations, translations etc. with retry strategies or other complex implementations.
EnterGPT does all of this for you in the background so you don't need to take care of it.