Spellforge

Easily customize and test GPT models with Spellforge.ai. Utilize synthetic users and auto quality evaluations for optimal performance.

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Spellforge's Top Features

No coding required

Supports OpenAI Custom GPT

Automated quality evaluation using GPT-4

Interface for various popular LLMs

Synthetic user persona simulations

Detailed AI performance insights

Easy modifications and personalization

Thorough simulation process

Early access availability

Reliability and readiness for real users

Frequently asked questions about Spellforge

Our primary focus has been on OpenAI Custom GPT.

To evaluate the quality of AI agent responses, we use GPT-4 along with our proprietary technique to assign a score between 0 to 100. This scoring system leverages GPT-4’s advanced understanding capabilities to analyze the relevance, coherence, and fluency of responses.

While our primary focus has been on integrating and specializing in OpenAI‘s LLMs, we have significantly broadened our scope. We’ve developed an interface that supports a wide variety of popular LLMs currently available in the field.

Synthetic user personas are used to simulate real-world user interactions, allowing you to test and refine your GPT model before deploying it to actual users.

No coding knowledge is required to use Spellforge. The platform allows for easy customization and testing of GPT models without any coding.

The simulation process may take some time to ensure thorough and accurate results, allowing you to thoroughly assess and refine your AI's performance.

Yes, you can easily make modifications and personalize your GPT model at any time by navigating to the 'Edit GPT > Configure' section in the platform.

Using synthetic users allows you to identify and address potential issues in your AI agent’s responses, enhancing reliability and performance before deploying it to real users.

You can analyze the overall results or delve into each conversation for a more detailed understanding of your AI’s performance, helping you identify areas for improvement.

If your GPT model's performance is not satisfactory, you can use the insights gained from the simulation results to make necessary adjustments and improvements to enhance its performance.