Why Anything?

The Challenge: Unreliable Software

In traditional software engineering, we rely on unit tests and deterministic outputs. assert(2 + 2 == 4) always passes.

Generative AI breaks this paradigm. 💥

LLMs are:

  1. Non-deterministic: The same input can yield different outputs.

  2. Unstructured: They output free text, not structured data.

  3. Hard to Control: Their behavior depends on prompts and context, which are often ambiguous.

This makes LLM-powered applications inherently unpredictable and hard to test.

Why not just use public benchmarks?

Public leaderboards (like HuggingFace Open LLM Leaderboard) measure generic model capabilities, not your application's performance.

  • Relevance: Knowing a model is good at high school math doesn't tell you if it will be polite to your customers.

  • Context: Benchmarks don't know about your RAG context, your system prompts, or your specific business rules.

  • Data Leakage: Public benchmarks are often contaminated.

You need to measure and monitor your specific use case. 🎯

However, finding the exact metrics to measure often takes time and iteration. You might start with generic checks and evolve into highly specific business rules as you learn more about your model's failure modes.

In short,

You want to measure and monitor your specific LLM-powered automation, not the generic academic capabilities of an LLM.

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