#eval-guardrails

Observability for LLM apps is the practice of collecting structured logs, traces, metrics, and feedback so you can see what an AI application actually did, why it behaved that way, and where it failed. LLM apps are harder to debug than traditional software because the “logic” is partly produced at runtime by a model. A user sees a bad answer, but the cause could be the prompt, retrieved context, tool output, model behavior, latency, token limits, or a broken chain of steps. Observability hel

LLM-as-a-judge is the practice of using a large language model to evaluate or rank outputs from another model, system, or workflow, instead of relying only on human reviewers or fixed metrics. It solves a common problem: many AI outputs are hard to score with simple automated checks. If you are judging a summary for usefulness, a chatbot answer for helpfulness, or two candidate responses for preference, exact-match metrics often miss the point. Teams reach for LLM-as-a-judge when they need: **Fa

An LLM eval, or evaluation, is a test or set of tests used to measure how well a large language model behaves on a task or under a specific set of conditions. If you build with LLMs, you need a way to answer practical questions like: Does this prompt change improve answer quality? Is the model following instructions more reliably? Did a new model version get worse on safety, latency, or factuality? An eval gives you a repeatable way to compare models, prompts, retrieval setups, or fine-tunes. In

A golden dataset or eval set is a small, carefully chosen set of examples with trusted labels or expected outputs that you use to judge whether a model, prompt, or system is working correctly. If you are building an LLM app, an agent, or a classic ML model, you need some repeatable way to answer: “Did this change make things better or worse?” A golden dataset gives you that baseline. Teams use it to: compare model versions or prompts catch regressions before deployment measure qualit

A guardrail is a rule, check, or control that keeps an AI system from producing or doing something unsafe, incorrect, or out of policy. Large language models and agents can generate useful outputs, but they can also hallucinate, leak sensitive data, follow malicious instructions, or take actions you did not intend. Guardrails help reduce that risk. You reach for guardrails when you need the system to stay within boundaries: for example, only answer from approved sources, refuse certain requests,

Hallucination detection is the process of spotting when an AI model’s answer is likely unsupported, false, or made up rather than grounded in the available evidence. Large language models can produce fluent answers that sound right even when they are wrong. Hallucination detection helps reduce the risk of shipping misleading outputs in search, customer support, medical/legal workflows, internal assistants, and any system where users may trust the model too much. In practice, teams use it when th

Regression testing for prompts is the practice of rerunning a fixed set of prompt-based checks after you change a prompt, model, tool, or surrounding code to make sure the system still behaves the way you expect. Prompted LLM systems are brittle in a different way than traditional software: a small wording change, a model upgrade, or a new tool call can improve one case and break another. Regression testing helps you catch those accidental behavior changes before users do. You’d reach for it whe