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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 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.
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.
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.
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.
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.
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.
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.
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.
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.
16 min readLLM SEO: How to Rank in Perplexity, ChatGPT, and AI Search in 2026
AI-powered search now drives over 100M queries per month. Here is how to get your content cited by Perplexity, ChatGPT Search, and Google AI Overviews.