https://llmjury.com/blog Run live A/B tests on your prompts and models, version prompts outside your codebase, and get a plain-English verdict backed by real statistics — LLM evaluation with proof, not gut feeling. en Fri, 07 Aug 2026 12:00:00 GMT https://llmjury.com/blog/how-to-choose-an-llm https://llmjury.com/blog/how-to-choose-an-llm Fri, 07 Aug 2026 12:00:00 GMT A leaderboard measures the model; your product is the model plus your prompt plus your traffic. Nine criteria with how to measure each on your own inputs — and why the prompt you tuned for the incumbent is a rigged instrument for testing a challenger. model selection evaluation experimentation https://llmjury.com/blog/evaluating-ai-agents https://llmjury.com/blog/evaluating-ai-agents Thu, 06 Aug 2026 12:00:00 GMT Outcome decides, trajectory explains, cost constrains. Why the outcome assertion has to come from outside the agent, why a golden path punishes a better route, and why nine sampling steps mean more traffic rather than less. agents evaluation metrics https://llmjury.com/blog/llm-non-determinism-testing https://llmjury.com/blog/llm-non-determinism-testing Wed, 05 Aug 2026 12:00:00 GMT Temperature 0 makes decoding greedy, not deterministic — and the reasons are floating-point, batching, and routing. The reframe that fixes it: one output against one output is n = 1, and nobody accepts that anywhere else in engineering. statistics evaluation prompt engineering https://llmjury.com/blog/can-you-trust-llm-as-judge https://llmjury.com/blog/can-you-trust-llm-as-judge Tue, 04 Aug 2026 12:00:00 GMT The objection is fair, and weaker than it looks. Position bias, verbosity bias, self-preference, rubric drift — and the reframe that resolves the argument: a judge doesn’t have to be right, it has to be wrong the same way for both arms. LLM-as-judge evaluation statistics https://llmjury.com/blog/how-much-traffic-do-you-need https://llmjury.com/blog/how-much-traffic-do-you-need Tue, 04 Aug 2026 12:00:00 GMT “We’re too small to A/B test” is usually wrong. A sample-size table for the metrics teams actually run — task success, judge quality, cost, latency — and the four levers that make small traffic workable. A/B testing statistics experimentation https://llmjury.com/blog/prompt-versioning-guide https://llmjury.com/blog/prompt-versioning-guide Tue, 04 Aug 2026 12:00:00 GMT A hardcoded prompt means a deploy to change a comma, no real rollback, no history — and, invisibly, no way to run two versions at once. The five properties a prompt version actually needs. prompt engineering prompt versioning production https://llmjury.com/blog/sample-ratio-mismatch https://llmjury.com/blog/sample-ratio-mismatch Tue, 04 Aug 2026 12:00:00 GMT A 3% imbalance on 100,000 users is not noise, it is a bug — and it invalidates every number in the experiment, not just the split. What causes SRM, why a timeout is the LLM-specific culprit, and why you must never reweight. SRM statistics experimentation https://llmjury.com/blog/model-upgrade-without-regressions https://llmjury.com/blog/model-upgrade-without-regressions Tue, 04 Aug 2026 12:00:00 GMT The new model benchmarks better and performs worse, because your prompt is a fossil record of the old model’s flaws. A five-step migration sequence with exit criteria, and why you should leave a comparison arm running forever. model migration A/B testing production https://llmjury.com/blog/cheaper-model-same-quality https://llmjury.com/blog/cheaper-model-same-quality Tue, 04 Aug 2026 12:00:00 GMT Four levers ranked by effort, and the statistical point that decides it: you are not asking whether the cheap model is better, you are asking whether it is worse by less than you will trade for the money. That is a non-inferiority test. cost A/B testing production https://llmjury.com/blog/when-can-you-call-it https://llmjury.com/blog/when-can-you-call-it Tue, 04 Aug 2026 12:00:00 GMT Fourteen daily looks at a p-value turns a 5% error rate into roughly one in five — and inflates every effect you report. Stopping rules that a real team will actually follow, plus the five lines to write down before you start. statistics experimentation A/B testing https://llmjury.com/blog/the-silent-regression https://llmjury.com/blog/the-silent-regression Tue, 04 Aug 2026 12:00:00 GMT No alert fires, because nothing breaks — the answers are still fluent, confident, and worse. Six causes with the detection method for each, and why a site-wide average is where regressions hide. production evaluation monitoring https://llmjury.com/blog/what-to-measure-llm-product https://llmjury.com/blog/what-to-measure-llm-product Tue, 04 Aug 2026 12:00:00 GMT Quality, operational, behavioural, business — four layers, what each answers, how fast it moves, and the trap in each. Plus why watching twenty metrics makes you less able to detect the effect you care about. metrics evaluation experimentation https://llmjury.com/blog/ab-testing-rag https://llmjury.com/blog/ab-testing-rag Tue, 04 Aug 2026 12:00:00 GMT Chunk size 512 because a blog post said so. Eight knobs, what each trades against, how to test it — and why retrieval metrics are diagnostic while end-to-end quality is the decision. RAG A/B testing evaluation https://llmjury.com/blog/measuring-hallucination-rate https://llmjury.com/blog/measuring-hallucination-rate Mon, 03 Aug 2026 12:00:00 GMT “Hallucination rate” is four different failures wearing one word. The taxonomy with a detection method for each, why groundedness has to be scored per claim rather than per answer, and why the absolute number belongs nowhere near a sales deck. evaluation LLM-as-judge RAG https://llmjury.com/blog/fine-tuning-vs-prompting https://llmjury.com/blog/fine-tuning-vs-prompting Sun, 02 Aug 2026 12:00:00 GMT “The prompt isn’t working” is four problems wearing one sentence, and fine-tuning fixes one of them. The symptom-to-technique table, the four costs that land after the training run, and why most published fine-tuning wins are measured against a first-draft prompt. fine-tuning model selection prompt engineering https://llmjury.com/blog/writing-judge-rubrics https://llmjury.com/blog/writing-judge-rubrics Sat, 01 Aug 2026 12:00:00 GMT Ninety per cent of your scores are a 4 because an unanchored scale measures the judge’s prior, not your product. An anchored 1–5 table you can copy, six rules for the rubric itself, and why the rubric, the model, and the temperature version as one unit. LLM-as-judge evaluation metrics https://llmjury.com/blog/ab-testing-chatbots-multi-turn https://llmjury.com/blog/ab-testing-chatbots-multi-turn Fri, 31 Jul 2026 12:00:00 GMT Sixty thousand turns is not sixty thousand observations. The unit-of-analysis error doesn’t make an experiment noisy, it makes it overconfident — plus which metrics live at turn, conversation, and user level, and why turns-to-resolution lies on its own. A/B testing experimentation statistics https://llmjury.com/blog/null-result-playbook https://llmjury.com/blog/null-result-playbook Thu, 30 Jul 2026 12:00:00 GMT “No significant difference” is three different results with the same p-value, and only the confidence interval tells them apart. What a null actually buys you, the four things not to do next, and why most flat results are a design problem rather than a traffic problem. statistics experimentation A/B testing https://llmjury.com/blog/structured-output-reliability https://llmjury.com/blog/structured-output-reliability Wed, 29 Jul 2026 12:00:00 GMT “Valid” is five different claims, and the one that hurts is a well-formed object with an invented enum member falling through a switch with no default. Why truncation is usually the culprit, and why compliance is the cheapest guardrail you can declare. production metrics evaluation https://llmjury.com/blog/segmenting-llm-experiments https://llmjury.com/blog/segmenting-llm-experiments Tue, 28 Jul 2026 12:00:00 GMT Almost every prompt change trades one segment against another, so a flat average is a suspicious result rather than a boring one. A worked Simpson’s paradox where the pooled split passes an SRM check, the segments worth declaring, and why a split result is a design brief. statistics metrics experimentation https://llmjury.com/blog/llm-latency-what-users-feel https://llmjury.com/blog/llm-latency-what-users-feel Mon, 27 Jul 2026 12:00:00 GMT TTFT, tokens per second, total completion, end-to-end — four numbers that diverge, and your interface decides which one matters. Why the tail is fat, why output length is the real lever, and why a latency gap between arms is usually an SRM. latency production metrics https://llmjury.com/blog/user-feedback-signals https://llmjury.com/blog/user-feedback-signals Sun, 26 Jul 2026 12:00:00 GMT 97% positive on a 1.5% response rate is a self-selected sample doing exactly what it should. Nine implicit signals with what each means and how each misleads, why a proxy needs validating once, and why behavioural metrics resolve before judge scores do. metrics evaluation production https://llmjury.com/blog/prompt-injection-monitoring https://llmjury.com/blog/prompt-injection-monitoring Sat, 25 Jul 2026 12:00:00 GMT Injection has no clean fix, so the answerable question is what fraction gets through and whether that moved. Blocked, deflected, complied, over-refused — and why the outcome nobody counts is the one that quietly costs you real users. security production monitoring https://llmjury.com/blog/bandits-vs-ab-testing https://llmjury.com/blog/bandits-vs-ab-testing Fri, 24 Jul 2026 12:00:00 GMT A bandit optimises, an experiment explains — and you get one. The comparison table, plus the reason bandits fit LLM products badly: they need fast feedback, and a judge score sampled at 5% on a batch is not fast feedback. A/B testing experimentation statistics https://llmjury.com/blog/multiple-experiments-at-once https://llmjury.com/blog/multiple-experiments-at-once Thu, 23 Jul 2026 12:00:00 GMT Independent randomisation makes a concurrent experiment noise rather than bias — so the default answer is yes, and queueing has a cost nobody puts on a dashboard. The four cases where it breaks, and the two-line salt bug that silently correlates every arm. experimentation A/B testing statistics https://llmjury.com/blog/prompt-caching-cost https://llmjury.com/blog/prompt-caching-cost Wed, 22 Jul 2026 12:00:00 GMT The cache breaks at the first token that differs, and everything below it is recomputed at full price — so one timestamp near the top of a 3,000-token system prompt voids the whole discount. Order by volatility, and track hit rate per variant. cost latency production https://llmjury.com/blog/testing-before-you-have-users https://llmjury.com/blog/testing-before-you-have-users Tue, 21 Jul 2026 12:00:00 GMT You can’t A/B test at forty users, which is not the same as being unable to measure. What each stage can and cannot tell you, the six things that work with no traffic, and why the plumbing is the expensive part rather than the statistics. evaluation experimentation prompt engineering https://llmjury.com/blog/eval-set-from-production https://llmjury.com/blog/eval-set-from-production Mon, 20 Jul 2026 12:00:00 GMT Invented examples measure how you handle a user who read the docs; model-generated ones share your model’s blind spot by construction. The five strata with their shares, why the rubric matters more than the labels, and the questions your product can’t answer. evaluation experimentation production https://llmjury.com/blog/reporting-experiment-results https://llmjury.com/blog/reporting-experiment-results Sun, 19 Jul 2026 12:00:00 GMT An experiment’s output is a decision plus the record that makes it re-examinable — otherwise the losing variant gets re-proposed in six weeks. A seven-section template, how to state uncertainty to people who won’t hear “significant” correctly, and the incentive check. experimentation metrics production https://llmjury.com/blog/logging-llm-traffic-privacy https://llmjury.com/blog/logging-llm-traffic-privacy Sat, 18 Jul 2026 12:00:00 GMT The numbers an experiment needs are not the text. The minimum viable event, field by field, with what is personal data and what isn’t — plus why a pseudonymous id is still in scope, and why text and metrics need completely different retention. privacy production https://llmjury.com/blog/why-ab-test-prompts https://llmjury.com/blog/why-ab-test-prompts Thu, 16 Jul 2026 12:00:00 GMT Ten playground outputs can’t tell you what a prompt change does to real traffic. The case for measuring every prompt edit the way you measure every other production change. A/B testing prompt engineering experimentation https://llmjury.com/blog/offline-evals-are-not-enough https://llmjury.com/blog/offline-evals-are-not-enough Thu, 16 Jul 2026 12:00:00 GMT Golden datasets are unit tests, not proof. Distribution drift, eval overfitting, and the metrics that only exist in production — latency, cost, and what users do next. evaluation LLM-as-judge production https://llmjury.com/blog/announcing-llmjury https://llmjury.com/blog/announcing-llmjury Wed, 01 Jul 2026 12:00:00 GMT Why “it feels better” is not an eval strategy, and how SRM gates, FDR correction, and an LLM judge make prompt experiments trustworthy. announcements statistics SRM FDR