1 min read
LLM Evaluation Journal: treating quality as a product metric
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
Evaluation
24 articles
1 min read
How I Evaluate LLM Changes: building eval sets from real user queries
I worked on smoothing the handoff between data engineering and AI teams—standardizing feature contracts, embedding validation, and adding lightweight…
1 min read
Improving Model Quality Without Guesswork: tracking groundedness before celebrating fluency
I focused on making delivery decisions auditable and repeatable—documenting intent, success criteria, and rollback paths to reduce tribal knowledge.
1 min read
LLM Evaluation Journal: reducing hallucinations through better test design
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
LLM Reliability in Practice: treating quality as a product metric
I spent the day reducing cognitive overhead for engineers and analysts—introducing clearer table contracts, simpler failure modes, and concise runbooks that…
1 min read
How I Evaluate LLM Changes: tracking groundedness before celebrating fluency
I tightened system boundaries so quality checks trigger earlier, catching regressions before downstream systems consume bad data.
1 min read
Continuous Evaluation: Monitoring AI Quality in Production
Continuous evaluation maintains AI quality standards throughout the system lifecycle.
1 min read
Evaluation Metrics for AI: Measuring What Matters
Regular evaluation with consistent metrics drives continuous improvement in AI quality.
1 min read
Weights and Biases for LLM Applications: Complete Guide
Weights & Biases provides a comprehensive platform for LLM observability, evaluation, and collaboration. Its strength lies in combining experiment tracking…
4 min read
Quality-Based LLM Routing: Maximizing Output Quality
Quality isn't one dimensional. Consider: Accuracy : Factual correctness Completeness : Covering all aspects Coherence : Logical flow and consistency…
1 min read
End-to-End RAG Evaluation: Complete System Assessment
Individual component metrics tell part of the story, but end-to-end evaluation measures how well your entire RAG pipeline performs as a system.
1 min read
Context Recall in RAG: Are You Finding All Relevant Documents?
While context precision measures noise in retrieved results, context recall measures completeness. Are you retrieving all the documents needed to fully…
1 min read
Context Precision in RAG: Evaluating Retrieval Quality
Context precision measures whether the retrieved documents are actually relevant to answering the question. High precision means less noise for the…
1 min read
Answer Relevancy in RAG: Does the Response Address the Question?
An answer can be factually correct and grounded in context but still fail to address what was actually asked. Answer relevancy measures how well the…
1 min read
Faithfulness Scoring in RAG: Detecting Hallucinations
Faithfulness is perhaps the most critical metric for RAG systems. An unfaithful answer that hallucinates information not in the source documents can be…
1 min read
Generation Metrics for RAG: Measuring Answer Quality
While retrieval metrics measure what documents are found, generation metrics evaluate the quality of the synthesized answer. This guide covers metrics…
1 min read
Retrieval Metrics for RAG: A Deep Dive
The retrieval component of RAG systems directly impacts generation quality. This guide provides a comprehensive overview of retrieval metrics and how to…
1 min read
RAGAS Framework: Automated RAG Evaluation
RAGAS (Retrieval Augmented Generation Assessment) is an open-source framework for evaluating RAG pipelines. This guide covers how to implement and use RAGAS…
1 min read
RAG Evaluation: Measuring Retrieval-Augmented Generation Quality
Retrieval-Augmented Generation (RAG) systems combine retrieval and generation components, each requiring specific evaluation strategies. This guide covers…
1 min read
Task-Specific Evaluation for LLMs: Beyond Generic Benchmarks
Generic benchmarks like MMLU and HumanEval don't predict performance on your specific use cases. This guide covers how to design and implement task-specific…
1 min read
MMLU Scores Explained: What They Really Mean
MMLU (Massive Multitask Language Understanding) is one of the most cited LLM benchmarks. Understanding what it measures and its limitations is crucial for…
1 min read
LLM Benchmarking: Understanding Model Performance
Understanding LLM benchmarks is essential for making informed model selection decisions. This guide covers the major benchmarks and how to interpret their…
1 min read
Model Evaluation Frameworks: A Comprehensive Guide
Rigorous model evaluation is critical for production AI systems. This guide covers the major evaluation frameworks and how to implement comprehensive…
1 min read
LLM Benchmarking: Evaluating Models for Your Use Case
LLM benchmarking methodology matters more than the benchmark scores themselves, because the standard public benchmarks (MMLU for general knowledge…