LLM Evaluation

AI agents are excellent at finding the obvious answer. That sounds like a compliment until the task is to avoid everyone else’s obvious answer. Imagine three firms using AI assistants to screen applicants, forecast demand, or decide which customer segments deserve attention. If the goal is consistency, shared focal points are useful. Everyone reads the same policy, applies similar criteria, and avoids the usual mess of human improvisation. Lovely. The spreadsheet smiles. ...

Memory sounds like a simple product feature. A user tells an assistant something today. The assistant remembers it tomorrow. Everyone applauds, the demo works, and someone writes “personalization” on a roadmap slide. Lovely. We have rediscovered a notebook. The harder problem begins when the user does not explicitly say what matters. A student says, “It’s fine.” A customer writes, “No worries.” A therapy-like support user replies with a short, polite sentence that looks neutral in isolation. Locally, the words are harmless. Historically, they may be resignation, guardedness, disappointment, or the emotional equivalent of quietly closing the door. ...

A name can do a suspicious amount of work. Give an LLM a table of colorectal cancer gene candidates and ask it to rank the best drug targets. When the gene names are visible, KRAS lands at #1. The model justifies the choice with a confident reference to “proven therapeutic tractability via covalent RAS inhibitors.” Sensible enough, if the task is to combine the supplied table with the model’s accumulated biomedical knowledge. ...

The checklist problem that exposes the model Checklist tasks look boring. That is exactly why they are useful. Ask an LLM to write a formal email under 50 words, include one required term, avoid another term, and return the result as JSON. None of this sounds intellectually difficult. No theorem proving. No multimodal reasoning. No dramatic benchmark leaderboard screenshot. Just instructions. ...

Teaching Minds or Just Mimicking? When LLMs Play Teacher Tutoring looks simple when the answer is already known. A student takes the wrong path. The teacher sees the better path. The teacher gives one piece of advice. Everyone nods, learning happens, and somewhere a product slide quietly adds “personalized AI tutor” beside a cheerful icon of a graduation cap. ...

Receipts are not glamorous. That is precisely why they are useful. A receipt-item categoriser is not a benchmark leaderboard, a launch demo, or a dramatic agentic workflow with a glowing dashboard. It is the kind of small, repetitive business decision that quietly determines whether an AI system becomes a product or remains an expensive toy. A bottle of iced coffee needs a category. A supermarket item needs to land in the right expense bucket. The output must be parseable. The cost must be low enough to repeat thousands or millions of times. Nobody wants a philosophical essay from the model. They want a JSON array. ...

A dashboard says revenue demand has “stabilized.” A monitoring agent says a sensor spike is “temporary.” A trading assistant says volatility has “fallen after the regime shift.” The sentence is smooth. The chart is nearby. The user is tired. That is usually enough for a bad explanation to survive. This is the quiet problem behind AI-assisted analytics: not whether a language model can write a plausible story about time-series data, but whether the story is faithful to the numbers. A recent paper, LLM-as-a-Judge for Time Series Explanations, studies exactly this gap by asking models to play two different roles: narrator and critic.1 ...

Replacement is a wonderfully clarifying business ritual. A vendor says its new model is better. The benchmark table agrees. The old system is slower, weaker, or less safe. Management asks for a recommendation. In ordinary software governance, this is dull but manageable: compare benefits, migration costs, risk, and timing. The incumbent system does not get a vote. It certainly does not write a memo explaining why its modestly inferior performance is, on deeper reflection, a sign of mature operational wisdom. ...

Compliance has a familiar little horror story: everyone can find the rule, but nobody can safely operationalize it. The document is searchable. The PDF is indexed. The chatbot can quote the right paragraph with the confidence of a junior associate who has just discovered Ctrl+F. And yet the actual business question still hangs in the air: who must do what, under which condition, subject to which exception, and with what consequence? ...

Tone is an attractive business lever because it feels cheap. No new model. No new data pipeline. No procurement meeting in which someone says “governance layer” with a straight face. Just add a more emotional sentence before the prompt and hope the model becomes sharper. This is exactly the kind of idea that spreads because it is easy to try and hard to interpret. One team finds that urgency helps. Another finds that politeness helps. A third discovers that telling the model you are scared improves one benchmark and damages another. Soon the organization has a secret prompt cookbook, which is always a classy substitute for measurement. ...