Are you looking to change the tone, style or output format of your LLM application to fit with your brand or app’s purpose? Behavioral Fine-Tuning of LLMs is the best way to accurately and consistently achieve this.

• Align tone and language with your domain
• Tune instruction-following behaviors into the model to correct instruction-following failures
• Align outputs to your desired format with much greater accuracy and consistency than prompt engineering could achieve on its own

1. We engage with you via call to gain a scope of what your project/app requirements are.
2. We create a training dataset for the model to learn from, which contains examples of the desired outputs
3. We create a separate validation dataset containing different data examples to test the fine-tuned model’s performance and generalisation to new but similar prompts.
4. We iterate on the original dataset and fine-tune a ‘fresh’ model again, continuing to test outputs using the validation set until the resulting LLM is validated against the performance metrics of the project
5. We invite you to test the model on our platform or via API to confirm its performance meets your criteria.