• A/B Testing Prompts: Systematically Getting Better ResultsLearn how to systematically A/B test prompts for better LLM results. Discover the right setup, measurable outcomes, and avoid common pitfalls.
• How to Evaluate an AI Agent: From Task Success to TrajectoryLearn how to test autonomous AI agents. Discover methods for trajectory analysis, tool evaluation, measuring step success, and use the practical evaluation…
• Benchmark contamination explained | llmnet.nl BenchmarkDiscover what benchmark contamination in LLMs means, how data pipeline leaks affect scores, and how to detect contamination effectively.
• Choosing benchmark datasets: does the test fit your question?How do you choose the right benchmark dataset for your AI problem? Avoid misleading scores, recognize contamination and match test data to production tasks.
• How Do You Read LLM Benchmarks? | llmnet.nl BenchmarkDiscover how to correctly interpret LLM benchmarks like MMLU and HumanEval. Learn to recognize pitfalls such as data contamination and cherry-picking.
• Benchmarks for Dutch-language model output | LLMnetAn overview of Dutch benchmarks, translation loss, LLM-as-a-judge for Dutch and setting up your own evaluation set.
• Bootstrapping confidence intervals for LLM testsLearn how to calculate non-parametric bootstrap intervals for LLM evaluations to substantiate noise, spread and measurement uncertainty quantitatively.
• Making bias in model output measurable | LLMNet BenchmarkMethodology for quantifying bias and stereotypes in language models through paired testing, Dutch context, and outcome measures.
• Comparing chunking strategies quantitatively for RAGHow do you quantitatively compare chunking strategies for RAG? A measurement method for retrieval statistics, context precision, and token costs.
• Evaluating Generated Code: Why 'It Compiles' Is Not EnoughLearn how to objectively evaluate generated code from LLMs. Discover the importance of pass@k, functional tests, style checks, and security audits for AI…
• Measuring drift in production: LLM performance over timeHow do you measure data drift, concept drift, and model drift for LLMs in production? Measurement methods, statistical tests, and pitfalls in practice.
• Setting up your own benchmark: a practical step-by-step planDiscover how to set up your own custom benchmark for LLMs. Follow our step-by-step plan from dataset design and metrics to reliable automation.
• Setting up an Elo rating system for model comparisonLearn how to set up a reliable Elo or Bradley-Terry rating system for pairwise LLM evaluations, including matchmaking, bias correction, and code.
• Evaluating Embedding Models for Search and RAG ApplicationsLearn how to objectively evaluate embedding models for RAG. Discover how to build a golden test set, metrics like recall, and the impact of the Dutch…
• From Production Logs to Evaluation DataLearn how to safely convert real user queries from production logs into a living test set, prevent regressions, and continuously improve your LLM application.
• Reporting evaluation results: from table to decision mapLearn how to turn raw LLM evaluation results into actionable decision maps. A practical method for well-founded model and provider choices.
• Evaluations in your pipeline: automatic testingAutomate LLM evaluations in your CI/CD pipeline. Monitor model quality, prevent regression on every prompt or model change and control evaluation costs.
• Testing function calling accuracy with schemasA measurement method for function calling accuracy with complex JSON schemas. Test nested objects, polymorphism, and strict validation systematically.
• Calibrating G-Eval criteria with human dataLearn how to precisely calibrate G-Eval scoring criteria and prompt rubrics using human annotation data and correlation statistics.
• Measuring Hallucinations: How Do You Test the Factuality ofDiscover how to measure hallucinations in Large Language Models (LLMs). Learn about factuality, groundedness, and view a practical test setup.
• Measuring the lost-in-the-middle effect in context windowsMeasure the lost-in-the-middle effect in long LLM context windows. Discover test matrices, noise distribution, statistical validation, and Python measurement scripts.
• Measuring instruction following with IFEvalHow do you measure whether a language model sticks to strict formatting rules? An in-depth analysis of the IFEval methodology for LLMs's.
• Indirect prompt injection: measuring resilienceLearn how to measure the resilience of LLM systems against indirect prompt injection systematically and repeatably through a structured benchmark.
• Calculating inter-annotator agreement for LLM juriesCalculate inter-annotator agreement for LLM juries using Cohen's kappa, Krippendorff's alpha, and pairwise consensus to validate evaluations.
• Evaluating JSON Validity Under Load | LLM BenchmarkEvaluating JSON formatting validity and schema compliance of language models under high concurrency. Distinguish infrastructure errors from model errors.
• Evaluating small models: where they surprise and where they failLearn how to systematically evaluate small language models. Discover where compact models perform surprisingly well and where they fail structurally in production.
• Comparing Cost per Task Between Models: A Fair MethodDiscover why the price per token is misleading and learn how to fairly compare the actual cost per task between AI models. Includes calculation method.
• Managing Evaluation Costs in LLM ApplicationsLearn how to manage LLM evaluation costs with proxy checks, smart budgeting, and a practical decision framework for every type of change.
• Quality versus cost: the trade-off in model selectionDiscover how to find the optimal balance between AI model performance and operational costs. Learn to choose strategically for the best price-performance…
• Measuring the Quality Impact of QuantizationDiscover how to accurately measure the effect of quantization on language model quality, where the first errors emerge, and which pitfalls to avoid.
• Measuring Latency with Percentiles Instead of Averages — LLM BenchmarkDiscover why averages fail for LLM latency and how to properly measure, aggregate, and translate p50, p95, and p99 into a reliable SLO.
• LLM-as-a-Judge: Models Judging Models | llmnet.nlDiscover how 'LLM-as-a-judge' works, what the bias risks are, and when automatic evaluation of AI models is reliable for your web application.
• Setting Up Human Evaluation: When You Need a JuryDiscover when you need a human jury for LLM evaluation, how to write annotation guidelines, and how to measure inter-rater reliability.
• Setting up human evaluation: annotation guidelines that workDiscover how to set up effective annotation guidelines for human LLM evaluation. Learn about inter-annotator agreement, pitfalls, and best practices.
• Model Quality per Task: One Score Says Too LittleWhy a single composite benchmark score misleads model selection. Learn to evaluate task-specifically with metrics for extraction, RAG, and reasoning.
• Evaluating Multimodal AI: Measuring ImageDiscover how to evaluate multimodal AI on image and audio input. Learn why text benchmarks fail and how to build a multimodal test set.
• Testing Dutch Language Proficiency of AI ModelsDiscover why and how to effectively test the Dutch language proficiency of AI models. Learn about dt-spelling errors, idioms, and build your own evaluation…
• Practically Measuring Dutch Translation QualityLearn how to practically and reproducibly measure translation quality into Dutch with test sets, automated metrics, and LLM-as-a-judge.
• Needle-in-a-haystack test method for LLM context windowsLearn how to use the needle-in-a-haystack method to test and quantify the true reliability of large context windows in language environments.
• Measuring and Neutralizing Position Bias in LLM-as-a-JudgeMeasure and neutralize position bias in pairwise LLM-as-a-judge evaluations with swap tests, statistical consistency measurements, and calibration.
• Prompt A/B Test Tool: Score Two VariantsCompare two prompt variants and model outputs live in your browser. Weighted scoring, text stats and an optional blind mode.
• RAG Evaluation: Measuring Retrieval and Generation QualityLearn how to effectively evaluate a RAG system by measuring retrieval (precision/recall) and generation (groundedness) separately and preventing regressions.
• Red teaming and safety testing for LLM applicationsDiscover how to test the safety of your LLM application before going live. Learn about prompt injection, building test sets, and automated scoring.
• Evaluating reasoning steps in math and logic tasksA guide to evaluating reasoning quality, intermediate steps, and answer accuracy in mathematical and logical LLM benchmarks.
• Regression testing for promptsDiscover how to set up regression testing for prompts to prevent quality loss during updates. Includes a practical test set, thresholds, and CI integration.
• Reproducibility of AI Evaluations: Same Test, Same ResultDiscover how to make AI evaluations fully reproducible. Learn all about temperature, seed settings, logging model versions, and practical checklists.
• Prompt caching simulator: measuring cost and latencyCalculate and simulate the real cost savings and latency reduction of prompt caching. Measure the impact of cache hit rates and prefix lengths.
• Latency, throughput and tokens/second: measuring LLM speedDiscover how to measure the performance of Large Language Models. Learn all about latency, tokens per second, throughput, and a fair measurement setup.
• Statistics for LLM EvaluationsUnderstand the statistics behind LLM evaluations. Learn about sample sizes, confidence intervals, and significance to compare models correctly.
• Determining Sample Size for LLM EvaluationsCalculate the exact sample size for reliable LLM evaluations. Prevent statistical noise, power errors, and unnecessary API costs.
• Quantifying sycophancy: measuring how readily a model agrees with the userLearn how to quantify sycophancy (yes-man behavior) in LLMs using controlled test sets, paired evaluations, and robust measurement statistics.
• Filtering synthetic evaluation data on densityHow do you filter synthetic evaluation sets by information density? Measure token entropy, semantic redundancy, and prevent data bloat in LLM benchmarks.
• Measuring system prompt variation and consistency | BenchmarkDiscover how to measure an LLM's sensitivity to system prompt variations and safeguard comparable outcomes during model evaluations.
• Calibrating temperature and top-p for determinismLearn to systematically calibrate temperature, top-p, and seed values for deterministic and reproducible LLM evaluations and reliable JSON extraction.
• Building your own test set without data contamination to theLearn how to build your own test set for LLM benchmarks without data contamination. Discover strategies for reliable and objective model evaluations.
• Measuring TTFT under varying server loadA measurement method for time to first token (TTFT) under varying server load. Analyze prefill delay, queuing, and percentiles in LLM inference.
• JSON schema output validator & benchmark toolValidate JSON schema output and benchmark multiple model results directly in your browser with this interactive client-side tool from llmnet.nl.
• Generator for needle-in-a-haystack testsInteractively build prompts for needle-in-a-haystack tests to examine the information processing and context range of language models.
• Reading and interpreting safety benchmarks for LLMsA critical guide to interpreting safety benchmarks for LLMs. Discover the pitfalls, testing methods, and Dutch-language nuance.
• Evaluating the translation quality of LLM outputAn analysis of methods for evaluating translation quality in LLMs, from n-gram overlap such as BLEU to neural metrics such as COMET and BLEURT.
• Evaluating Your Own LLM: A Practical Evaluation FrameworkA practical and directly applicable framework for evaluating Large Language Models. Includes test sets, core criteria, and an example scorecard.