The Traceloop Blog

A/B testing prompts isn’t just about swapping lines of text. It’s a structured process. This guide breaks down the five key steps to running reliable A/B tests for LLM prompts in production, from forming a clear hypothesis and defining robust metrics to splitting traffic safely and analyzing results with statistical rigor. Learn how modern platforms like Traceloop simplify prompt management, evaluation, and deployment so you can continuously improve user satisfaction and performance.

RAG apps can break in production even when the code hasn’t changed. The culprit is rarely a bug. More often, the cause is data drift, concept drift, flawed chunking, embedding limitations, noisy context, or external dependencies. This article explains the hidden causes of silent degradation in RAG systems and how continuous monitoring and evaluation can help keep outputs accurate, relevant, and trustworthy over time.

Learn how to evaluate whether your LLM outputs are truly satisfying users. This guide breaks down a practical 5-step framework to measure user satisfaction, track implicit signals like retries and abandonment, detect hallucinations, collect explicit feedback, trace root causes, and build a continuous improvement loop. Perfect for teams looking to turn unpredictable LLM behavior into reliable, high-performing applications.

Traceloop auto-instruments your LangChain RAG pipeline, exports spans via OpenTelemetry, and ships ready-made Grafana dashboards. Turn on the built-in Faithfulness and QA Relevancy monitors in the Traceloop UI, import the dashboards, and set a simple alert (e.g., > 5 % flagged spans in 5 min) to catch and reduce hallucinations in production, no custom evaluator code required.

Tokenization, the process of breaking text into smaller units called tokens, forms the foundation of how Large Language Models like BERT and GPT understand and generate human language. This article explores different tokenization algorithms, their applications in Natural Language Processing tasks, common challenges, and how they compare to other text evaluation metrics, highlighting tokenization's crucial role in bridging human and machine understanding.