• A/B Testing Prompts: Systematically Getting Better Results | llmnet.nlLearn 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 Trajectory Analysis | LLMnet.nlLearn how to test autonomous AI agents. Discover methods for trajectory analysis, tool evaluation, measuring step success, and use the practical evaluation form.
• 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.
• Evaluating Generated Code: Why 'It Compiles' Is Not Enough | LLMnetLearn how to objectively evaluate generated code from LLMs. Discover the importance of pass@k, functional tests, style checks, and security audits for AI code assistants.
• 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 language.
• From Production Logs to Evaluation Data: Closing the Feedback Loop | LLMnetLearn how to safely convert real user queries from production logs into a living test set, prevent regressions, and continuously improve your LLM application.
• Measuring Hallucinations: How Do You Test the Factuality of an LLM? | llmnet.nl BenchmarkDiscover how to measure hallucinations in Large Language Models (LLMs). Learn about factuality, groundedness, and view a practical test setup.
• Comparing Cost per Task Between Models: A Fair Method | benchmark.llmnet.nlDiscover 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 selection - LLMNet BenchmarkDiscover how to find the optimal balance between AI model performance and operational costs. Learn to choose strategically for the best price-performance ratio.
• LLM-as-a-Judge: Models Judging Models | llmnet.nlDiscover how
• Setting up human evaluation: annotation guidelines that work | LLMNet BenchmarkDiscover how to set up effective annotation guidelines for human LLM evaluation. Learn about inter-annotator agreement, pitfalls, and best practices.
• Evaluating Multimodal AI: Measuring Image, Audio, and Combined Tasks | LLMnetDiscover 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 Models | LLMnet BenchmarkDiscover why and how to effectively test the Dutch language proficiency of AI models. Learn about dt-spelling errors, idioms, and build your own evaluation set.
• 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 applications: How to test before going liveDiscover how to test the safety of your LLM application before going live. Learn about prompt injection, building test sets, and automated scoring.
• Regression testing for prompts: preventing quality from silently dropping | LLMNet BenchmarkDiscover 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.
• Latency, throughput and tokens/second: measuring LLM speed | Benchmark LLMnet.nlDiscover 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 Evaluations: How Many Test Questions Do You Really Need?Understand the statistics behind LLM evaluations. Learn about sample sizes, confidence intervals, and significance to compare models correctly.
• Building your own test set without data contamination to the model | llmnet.nlLearn how to build your own test set for LLM benchmarks without data contamination. Discover strategies for reliable and objective model evaluations.
• Evaluating Your Own LLM: A Practical Evaluation Framework | LLMnet BenchmarkA practical and directly applicable framework for evaluating Large Language Models. Includes test sets, core criteria, and an example scorecard.