Large language models (LLMs), such as GPT-4, are intelligent tools that allow for rapid, cost-effective solution-building, setting the stage for LLM-driven applications to dominate your company's software landscape. However, the remarkable reasoning power of these models isn't without flaws, as they may produce inconsistent outputs, hallucinate, or even deceive.
Predictability and consistency are paramount in crafting dependable systems, posing a challenge given the aforementioned inconsistencies. The solution? Evaluation frameworks.
These frameworks act as essential checkpoints for your LLM system, enabling you to gauge the effects of changes, including new models or altered prompts. As a vital component of your application, the absence of such evaluation can cause your progress to stall.
In Episode 6 of our AI strategy series, I illustrate the creation of a basic evaluation framework. I designed five scenarios, merging various models and LLM agent instructions, and assessed them using four metrics:
The findings may astonish you, as they did me, driving home the indispensable need for an evaluation framework in your operations.