The problem

"Why are engineering hours going into writing prompts and testing responses?"

"Engineering bandwidth is being sucked into writing and optimizing prompts, testing different models, and doing QA / manual review of AI generated responses"

One of the biggest challenges we see in the AI development lifecycle is that non-technical users are unable to experiment and test effectively.

This means engineers are responsible for iterating on prompts, running experiments, testing different models, and often even QA-ing the responses.

This engineering bottleneck also creates very slow iteration cycles for teams because there's no easy way for engineers, data scientists, PMs and QA to collaborate on AI product development and testing.

The solution: Athina IDE

One of the CORE value props of Athina IDE is that non-technical users like PMs are also first-class citizens on the platform.

A Product Manager with access to Athina can:

• Run prompts on a dataset
• Prototype chains of prompts, API calls, retrievals, etc
• Experiment with dozens of different models
• Run preset evaluations or create custom evals