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Let me know if this sounds familiar:

We spent months setting up our LLM application backend and integrating it into our frontend, but it ended up not being accurate enough to be useful in production.

We, engineers, tend to approach LLM development as an engineering problem, as we do with most traditional software applications. This means we focus on wiring up a bunch of components together such as the OpenAI API, Vector DB, backend, frontend, auth, security, scalability, etc., and expect the application will just work. However with LLM development, there’s a second piece of the puzzle that comes after the engineering — making the application accurate. This part is often glossed over and not explored until after initial development is complete. As a result, teams end up spending 3-6 months on LLM development, just to realize what they’ve built is not useful for production. This is typically when they begin trial and erroring through accuracy improvements, using “vibe-checks” with little success.

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Solution

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Accuracy is the most important part of LLM development -- without an accurate application, the product is useless. The best way to improve accuracy is to systematically run numerous experiments, empirically testing the impact of changes to your LLM stack on your output. For example, how does adding a new paragraph to my prompt affect the overall accuracy of my application? We are working on a Framework that takes an accuracy-first approach from the beginning of your LLM Development journey. We do this by structuring your LLM development for rapid experimentation, providing you with all the tools needed to manage and evaluate experiments at scale, and helping you deploy your application to production.

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Getting Involved

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- Contribute, Create Issues, Star it on GitHub

- Share Your Thoughts