LLM Testing
Open-source LLM evaluation tools matter for teams that can't send production data to third-party cloud services, that need to self-host their observability stack, or that simply want full control over their evaluation pipeline. Opik, from Comet, is one of the most capable open-source options in this space. It
LLM Testing
Conversational AI applications present a testing challenge that single-turn LLM pipelines don't: the context of a full conversation matters. What a user said three turns ago can determine whether turn seven's response is correct or wrong. Standard observability tools that log individual LLM calls miss this.
LLM Testing
Every LLM API call your application makes is a potential point of failure, a cost you're incurring, and a data point you could be learning from. Most teams log almost none of this. Helicone is a proxy layer that sits between your application and LLM providers — capturing everything,
LLM Testing
LLM applications introduce a new category of failure that traditional testing tools were never built to catch. A response that was accurate yesterday might drift subtly today — same prompt, different model behavior. Tracing, evaluation datasets, and online scoring are how production teams stay ahead of that drift. Langfuse is one
LLM Testing
The hardest thing about deploying LLM-powered features isn't building them — it's knowing whether they're getting better or worse. Prompt tweaks, model upgrades, retrieval changes: each one can improve performance on some inputs while degrading it on others. Braintrust gives you the infrastructure to measure
LLM Testing
Testing an AI agent is not like testing a traditional API. When a REST endpoint misbehaves, you read the logs and find the bad request. When an AI agent misbehaves, you're staring at a black box that made a sequence of decisions — each one plausible in isolation, wrong