AI Scoring & Evals
Benchmarks explained, evaluation frameworks, model testing
9 min readSWE-Bench: The Gold Standard for Evaluating LLM Software Engineering
SWE-Bench tests LLMs on 2,294 real GitHub issues from popular Python repositories, evaluating whether the model can write code that passes the existing test suite - a far harder and more realistic evaluation than HumanEval.
9 min readLLM Red Teaming: How to Find Failure Modes Before Your Users Do
Red teaming is adversarial testing designed to find safety, reliability, and robustness failures in LLM applications before they reach production. Here is how to run a systematic red team exercise.
8 min readVibes vs. Benchmarks: Why You Need Both to Test LLMs
Neither informal testing nor published benchmarks alone can tell you whether a model is right for your use case. The right process uses both, in a specific order.
6 min readPromptFoo: The Best Open Source Tool for LLM Prompt Evaluation
PromptFoo lets you define test cases in YAML, run them against multiple models and prompt variants in parallel, and get comparison reports in minutes. Here is a complete setup guide with real-world examples.
8 min readPrecision, Recall, and F1 Explained for LLM Evaluation
Precision, recall, and F1 are the foundation of retrieval evaluation. Understanding the tradeoff between them tells you whether to optimize your RAG system for fewer wrong answers or fewer missed answers.
5 min readHow LMSYS Chatbot Arena Works and Why It Matters
Chatbot Arena ranks LLMs through millions of real user preference votes rather than fixed benchmarks. It is the most contamination-resistant ranking system that exists today.
9 min readA/B Testing LLM Outputs in Production
A/B testing LLM changes in production is how you confirm that a new model or prompt actually improves business outcomes. Here is the setup, what to measure, and the common mistakes that invalidate results.
8 min readMMLU, HumanEval, and Chatbot Arena Explained: What AI Benchmarks Actually Measure
A plain-English explanation of every major LLM benchmark: what each one tests, how it scores, and what a 1% difference actually means in practice.
9 min readHow to Build a Complete Evaluation System for a Production LLM App
A production eval system has three layers: offline testing before deploy, online monitoring in production, and a feedback loop that turns failures into new test cases. Here is how to build all three.
8 min readLM-as-Judge: Using LLMs to Evaluate LLM Outputs
LM-as-judge works well for relative preference ranking but breaks down for absolute quality scores. Here is how to set it up and avoid the major failure modes.
7 min readSWE-Bench Explained: The Hardest Benchmark for AI Coding
SWE-Bench uses real GitHub issues from real projects to test whether models can write code that actually fixes software bugs. It is far more demanding than HumanEval.
9 min readBuilding an LLM Evaluation Pipeline From Zero
How to build an eval system that catches 80% of regressions with 20% of the effort. Start with real production examples, define clear scoring, and track it over time.
8 min readTruthfulQA Explained: Why Bigger Models Are Not Always More Truthful
TruthfulQA measures whether models give truthful answers to questions humans often get wrong due to misconceptions. Its key finding - larger models can be more convincingly wrong - has real implications for high-stakes use cases.
13 min readHow to Evaluate LLMs: Benchmarks, Vibes, and Building Your Own Evals
Benchmarks are gamed and vibes do not scale. Here is how to build real evaluations that tell you whether an LLM actually works for your specific use case.
8 min readRAGAS: The Standard Framework for Evaluating RAG Systems
RAGAS gives you four metrics that cover every major failure mode in a retrieval-augmented generation pipeline. Here is what each metric measures and how to act on low scores.
11 min readRAG Evaluation: Implementing Ragas and TruLens for Production Pipelines
How to systematically measure retrieval context relevance, generation faithfulness, and answer correctness in production search systems.
10 min readEvaluation Metrics for Code-Generation LLMs: Beyond HumanEval Benchmarks
Standard benchmarks like HumanEval do not reflect real coding. Explore AST verification, compile checks, and unit test runners for agent evals.
9 min readAutomated Red-Teaming for LLMs: Simulating Attacks at Scale
Use secondary LLMs to systematically probe your production application for vulnerabilities, offensive outputs, and jailbreak weaknesses.
8 min readSemantic Drift in LLMs: Tracking Changes in Model Outputs Over Time
Models updated silently behind APIs can drift in behavior. Learn how to trace embedding vectors to catch changes in answers.
9 min readMMLU Explained: What the 57-Subject LLM Benchmark Actually Tests
MMLU covers 57 academic subjects with 15,908 multiple-choice questions from elementary to professional level, and remains one of the most widely cited LLM benchmarks despite significant criticisms.
9 min readThe Needle-in-a-Haystack Test: Benchmarking LLM Long-Context Recall
The needle-in-a-haystack test measures whether an LLM can recall a single specific fact embedded at varying depths in a long document, revealing which models have uniform long-context recall and which have blind spots.
6 min readPromptfoo: Test and Red-Team Your LLM Prompts Before Shipping
Promptfoo runs your prompts against multiple models, checks outputs with assertion functions, and red-teams for jailbreaks and PII leakage - all from a YAML config.
7 min readDeepEval: Write Unit Tests for LLMs Like You Write Tests for Code
DeepEval integrates with pytest to give LLM responses the same test coverage discipline as regular code - hallucination checks, bias detection, and CI-gated quality gates.
7 min readRAGAS: The Standard Framework for Evaluating RAG Pipelines
RAGAS gives you four principled, LLM-computed metrics - faithfulness, answer relevancy, context precision, and context recall - to objectively score your RAG system.