The Eval Scorers You Actually Need for LLM Apps (and When to Use Each)
Most LLM eval advice collapses into one of two failure modes. Either someone tells you to "just use an LLM judge for everything," or someone hands you a wall of BLEU and ROUGE scores that nobody on the team can interpret. Both are wrong for the same reason: a scorer is a claim about what "correct" means for a specific output, and no single scorer makes that claim well for every case. I have spent…
When evaluating outputs from large language models (LLMs), it's crucial to use the appropriate scoring method for each type of error. Generic scorers, such as LLM judges or a wall of BLEU and ROUGE scores, are inadequate because they make a single claim about correctness that doesn't hold true for every case.
The key is to identify the specific type of error and select the cheapest deterministic scorer that accurately measures it. Deterministic scorers are fast, free, and never flake, while probabilistic scorers like embeddings and LLM judges catch nuance but come at a cost and introduce variance.
A useful approach is to categorize scorers based on the question they answer. For exact-match outputs, such as fixed greetings or routing labels, use an exact-match scorer. For structured data fed to downstream systems, use a JSON schema scorer. For meaning but flexible wording, use embedding similarity, and for subjective quality like tone or helpfulness, use LLM judges. Latency and cost are also important quality dimensions to consider when selecting scorers.
In voice applications, additional scorers are necessary. Voiceeval includes checks for misheard numbers, failing to read back a value before taking a consequential action, slow responses, dead air, and policy violations. These voice-specific scorers address failures that generic text scorers would miss.
The lesson is to evaluate each output based on its unique characteristics and select the most appropriate scorer for the specific question at hand. By using the right scorers for the right questions, you can effectively evaluate LLM outputs and ensure high-quality results.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
