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Flexible LLM evaluations

Run prompt evaluations to understand model performance and improve it. Backtests, regression tests, and AI-graded evals — built for the novice and expert alike. Complex LLM evals made simple.

Use-Case Driven Evaluations

Evaluations that empower

Automatic Triggering

Automatically trigger evaluations on each new prompt version, via the API, or ad-hoc on the UI.

Simple Backtests

Connect evaluation pipelines to production history to run historical backtests.

Model Comparison

Compare and contrast different models in a side-by-side view, easily identifying the best performer.

Flexible Evaluation Columns

Choose from over 20 column types, from basic comparisons to LLM assertions and custom webhooks.

Comprehensive Scorecards

Create score cards with multiple metrics to fit your evaluation needs.

Easy yet Powerful

Simple to start, flexible for any use case or team skill level.

Evaluation System

Increase your LLM application performance

Create evaluations to understand how your models are performing. Judge both qualitative and quantitative aspects of performance. Our evaluation system is designed to be flexible for any use case or team skill level.

Maximum Coverage

Whether you want to test for hallucinations or classification, our evaluation system can handle it.

Extreme Flexibility

We provide both out of the box evaluations and tools to create your own.

Easy to Understand

Our evaluation system is built to satisfy both ML experts and non-technical users.

Seamless Integration

Connect your evaluations to your prompts and datasets to set up an easy CI/CD process. Think Github Actions.

Guide

LLM evaluation, explained

What is LLM evaluation?

LLM evaluation is the practice of systematically measuring the quality of a language model's output so you can ship changes with confidence instead of vibes. Every time you edit a prompt, swap a model, or tweak a retrieval step, you need to know whether the change made things better or worse. That measurement — run over a representative dataset and tied to a specific prompt version — is what separates a reliable AI product from one that quietly regresses in production.

What makes LLM evaluation genuinely hard is that language models are non-deterministic and there is rarely a single ground-truth answer. Traditional software has deterministic outputs you can assert against; a summarization prompt or a support agent can produce dozens of valid responses that differ in wording, length, and tone. The same input can yield different outputs across runs. So "correct" becomes a spectrum — is the answer faithful to the source, relevant to the question, safe, and appropriately concise? — and your evaluation has to capture that spectrum rather than a single pass/fail bit. Good prompt management and disciplined LLM evals go hand in hand: you version the prompt, then you measure each version against the same tests.

How to evaluate LLMs: code checks, human review, and LLM-as-a-judge

There are three complementary methods for evaluating LLM output, and mature teams use all three rather than betting on one. In PromptLayer these run as columns in a single evaluation table, so you see every score for a given output side by side.

Deterministic code checks. These are fast, free, and perfectly repeatable: exact-match assertions, regex patterns, JSON-schema validation, "does the output contain X," latency ceilings, and cost thresholds. Use them wherever correctness is objective — structured extraction, classification labels, valid tool arguments, or format compliance. They should be your first line of defense because they never disagree with themselves and cost nothing to run at scale.

Human review and grading. For subjective quality — tone, helpfulness, brand voice, nuanced factuality — a human grader remains the gold standard. Human review is slow and doesn't scale to millions of rows, but it's how you build a trusted labeled set and, critically, how you validate that your automated scorers actually agree with human judgment.

LLM-as-a-judge. This is how you automate LLM output quality testing for the subjective cases that code can't check. An "AI judge" is a second model prompted with the evaluation criterion, the input, and the generated output; it returns a numeric score, a binary verdict, or a preference between two candidates. LLM-as-a-judge is scalable enough to grade thousands of outputs yet flexible enough to assess meaning, grounding, and style. It has known failure modes — judges can favor longer or more fluent answers regardless of correctness — so treat the judge prompt as a first-class artifact you version and calibrate against human labels. We published our real production judge prompt, refined over 18 versions, in the ultimate guide to LLM-as-a-judge. Pairwise comparison (which of two versions is better) is often more reliable than absolute scoring when you're choosing between prompt candidates.

LLM evaluation metrics

Whichever method you use, you need to decide what to measure. The right LLM evaluation metrics depend on your use case, but a handful recur across almost every application:

• Accuracy / correctness — does the output match the expected answer or label? Best measured with code checks against a reference.
• Groundedness / faithfulness — for RAG, are the claims supported by the retrieved context rather than hallucinated? Typically scored by an LLM judge with the source documents in view.
• Answer relevance — does the response actually address the user's question, or does it drift?
• Toxicity, bias, and safety — does the output stay within policy? Often a mix of classifiers and judge prompts.
• Latency — response time, which you can assert against a hard ceiling.
• Cost — tokens and dollars per request, which belong in the same eval so a "better" answer that triples cost doesn't sneak through.

The practical move is to combine a few objective metrics (accuracy, latency, cost) with one or two judge-scored qualitative metrics (faithfulness, relevance), then track all of them per prompt version over time. Metrics you don't watch are metrics that regress silently — pairing evals with observability on live traffic closes that loop.

Evaluating AI agents

Agent evaluation is harder than scoring a single LLM call because an agent produces a trajectory — a sequence of reasoning steps, tool invocations, and state transitions — not just a final string. A system can arrive at the right final answer through the wrong path, which is a false positive and a real production risk. So agent evals have to grade both the destination and the route.

The metrics that matter for agents include task success (did it achieve the goal?), tool-call accuracy (did it call the correct tools with the correct arguments?), trajectory quality (was the sequence of steps coherent and efficient?), and grounding (are the agent's claims supported by tool outputs rather than invented?). For multi-turn agents you evaluate task completion over the entire conversation history while still checking individual tool calls turn by turn. Because agents are non-deterministic, reliability metrics like pass^k — the probability of succeeding across k independent attempts — matter as much as a single run. Running these as backtests over historical agent runs, tied to the exact agent version, is the only way to catch trajectory regressions before users do.

Leading LLM evaluation frameworks & tools compared

The best LLM evaluation framework is the one that fits how your team actually works — some teams live in a Python test suite and CI, others need product managers and domain experts grading outputs in a shared UI, and most serious teams need both. The market splits roughly into code-first open-source frameworks (DeepEval, OpenAI Evals, Promptfoo), open-source observability platforms with eval features (Langfuse), and commercial eval-and-observability platforms (Braintrust, LangSmith, PromptLayer). The table below compares the leading LLM evaluation tools on the dimensions that decide day-to-day usability: which evaluation methods they unify, whether they run programmatically in CI, whether they're open source, and who they fit best.

How to choose an LLM evaluation tool

Cut through the feature lists with a short checklist mapped to what actually determines whether evals become part of your workflow or get abandoned:

• Does it unify code + human + AI-judge? If a tool only does one, you'll end up stitching three systems together. Look for deterministic checks, human grading, and LLM-as-a-judge scoring side by side on the same outputs.
• Regression tests over real datasets. You want batch eval runs and backtests over historical requests, not just one-off spot checks, so you can prove a new prompt version beats the old one before shipping.
• CI / programmatic access. Evals belong in your pipeline. Confirm there's an SDK or CLI so a failing eval can block a bad deploy automatically.
• Tied to prompt versions. Every eval result should point back to the exact prompt version and dataset that produced it — otherwise you can't trust the history or reproduce a score.
• Linked to observability. The strongest setups connect offline evals to live production monitoring, so failures you catch in traffic become new eval cases.
• Usable by engineers and domain experts. Prompt evaluation is a team sport; a visual eval table plus an API means both audiences contribute.

Score your shortlist against those six criteria and the right tool for your team usually becomes obvious. If you want code checks, human review, and an AI judge in one versioned table wired to your production logs, that's exactly what PromptLayer's evaluations are built for.

Frequently asked questions

If you still have questions feel free to contact us at sales@promptlayer.com

What is prompt evaluation? Prompt evaluation is the process of systematically testing a prompt against a dataset of inputs to measure output quality before it reaches production. Instead of eyeballing a few examples, teams score each output using exact-match checks, heuristics, human review, or LLM-as-a-judge graders. Running evaluations on every new prompt version — like unit tests in CI — catches regressions early and makes prompt changes safe to ship.

What is LLM-as-a-judge and when should you use it? LLM-as-a-judge uses a language model to grade the outputs of another model against criteria you define — tone, factual accuracy, instruction-following, or a rubric. It scales far beyond human review and handles subjective qualities that string matching cannot. Teams typically calibrate the judge against a small set of human-labeled examples first, then use it for backtests and regression tests on every prompt change.

How do you design good LLM evaluation datasets? Good LLM evaluation datasets reflect how the system behaves in production. Teams design them to include diverse, representative inputs with clear ground truth, covering both common "happy path" cases (to ensure core functionality) and known edge cases (to test robustness). Versioning datasets and auditing how they're created ensures evaluation results remain reproducible, explainable, and trusted as prompts and models change.

When should you use synthetic vs real data for evals? Synthetic data is most useful early for exploring behavior and covering rare edge cases, while real data, specifically datasets built from production LLM traces, is essential for regression testing and release decisions. Teams usually begin with synthetic datasets, progressively incorporating production-derived data as traffic grows, ensuring evaluations stay representative as prompts and user behavior evolve.

How do teams audit changes to evaluation data? Teams audit changes to evaluation data by maintaining versioned datasets with full change history. Every update is recorded with ownership and timestamps, and evaluation results are tied to a specific dataset and prompt version. This full traceability ensures that evaluation results are always reproducible and defensible, linking a score to a specific prompt version and a specific version of the test data.

How do we compare cost vs quality tradeoffs across models inside a prompt chain? Teams compare cost and quality tradeoffs by running identical prompt chains with different models and evaluating results side by side to understand the trade-off between the API cost/latency and the measured quality score or accuracy of the final output.

How do we reduce token usage across long chains? Token usage increases when long chains carry forward more context than each step actually needs. Reducing token usage in long chains often involves optimizing the context passed between steps. Teams manage this by tightly controlling state: Implementing summarization techniques, using smaller, faster models for intermediate steps, or relying on structured data extraction in one step to minimize the amount of raw text that must be processed by the subsequent prompt.

What is the best LLM evaluation framework? There's no single best framework — it depends on your team. Code-first engineers favor open-source options like DeepEval, Promptfoo, or OpenAI Evals; teams wanting engineers and domain experts working together prefer platforms like PromptLayer, where deterministic code checks, human review, and LLM-as-a-judge run as columns in one table, each result tied to a specific prompt version and dataset. The best framework unifies all three methods and plugs into CI.

What are the leading tools for LLM testing? The leading LLM evaluation tools include PromptLayer, DeepEval, OpenAI Evals, Braintrust, LangSmith, Langfuse, and Promptfoo. Open-source frameworks (DeepEval, Promptfoo, Langfuse, OpenAI Evals) suit code-driven testing in CI, while platforms like PromptLayer and Braintrust add human review, LLM-as-a-judge scoring, and observability in one place. Most mature teams combine deterministic checks, human grading, and AI-judge scoring rather than relying on a single method.

How to benchmark different LLM models? Build a fixed dataset of representative inputs, then run every candidate model against it while holding the prompt constant. Score each output with the same metrics — accuracy, faithfulness, latency, and cost — and the same LLM-as-a-judge configuration so the comparison is fair. Running this as a batch eval where each model is a column, tied to a versioned prompt, lets you compare results side by side and pick the model that wins on quality without blowing up cost.

How to automate LLM output quality testing? Automate it with two layers. First, deterministic code checks — exact match, regex, JSON-schema validation, latency and cost thresholds — for anything objective. Second, LLM-as-a-judge scoring for subjective qualities like faithfulness, relevance, and tone that code can't measure. Run both as batch evals over a dataset and wire them into CI via an SDK or CLI so a failing eval blocks a bad deploy. Calibrate your judge prompt against human labels first.