llm as judge

Evaluation is the hardest, most underrated part of shipping LLMs. The signal is knowing why public benchmarks mislead, why LLM-as-judge is biased, and how to build a task-specific eval you actually trust.

LLM-as-judge is how you grade open-ended generation at scale, and G-Eval formalized it. The signal is naming the biases that make raw judge scores untrustworthy and showing exactly how you harden them.

'Eval is the new system design' for LLM apps, and starting from zero data is the real test. The signal is bootstrapping a golden set, picking task-specific metrics, and running evals continuously as a regression gate.

Pairwise asks which is better, pointwise asks how good. The signal is knowing which is more reliable, how Elo/Bradley-Terry aggregates comparisons, and where each method quietly misleads.

RLAIF replaces expensive human labels with an LLM's preferences, which scales but inherits the labeler model's biases. The signal is how AI feedback is collected and where it quietly fails.

A VLM that reads an invoice wrong and a VLM that reads it right and reasons wrong produce the same end-to-end score, and they need opposite fixes. Here is how to decompose a multimodal eval, build the set from real traffic, and probe for the hallucinations that accuracy never catches.

Most RAG systems ship with no real eval, which is why most RAG systems quietly degrade. The signal is decomposing evaluation into retrieval and generation, and measuring faithfulness separately from relevance. Here is the framework that makes RAG improvements measurable.

Single-turn evals cannot catch failures that history causes, because they have no history. The offline answer is two suites, one deterministic and one simulated, and an honest account of why the simulated user will lie to you about how good your agent is.

Every team eval-ing prompts in ad-hoc notebooks is how an org ships regressions and argues about vibes. A shared eval platform makes quality measurable and comparable. Here is what it has to provide.

An AI support agent handling millions of chats will sometimes be wrong, rude, or unhelpful, and you cannot read them all. Monitoring quality at that scale is its own design problem with its own failure modes.

Eyeballing a few outputs does not scale, and a prompt tweak that fixes one case quietly breaks ten. A real LLM eval pipeline is a versioned dataset, layered scorers, and a CI gate. Here is the architecture.