Library of pre-built evaluators
Faithfulness, answer relevance, tool-choice correctness, JSON schema validity, toxicity, PII leakage, tone, format compliance
EVALUATIONS
Stop shipping prompt changes on vibes.
Run pre-built or custom evaluators against test datasets, experiment runs, or live production traffic — and grade every change before it ships and every response after it does.
Deep dive
Evaluation explained
"It worked in testing" is how compliance violations, hallucinated refunds, and 3 a.m. pages happen. Most teams catch issues only after a user complains — because they have no systematic way to grade quality.
Netra makes evaluation a first-class step in your development loop. Define what good looks like for your agent — faithfulness to retrieved context, correct tool selection, valid JSON, on-policy tone, no leaked PII — then grade every change against it. Pick from our evaluator library, or write a custom one in Python and run it on the same infrastructure.
Run evaluations three ways: on datasets for offline regression testing before you ship, on experiment runs to compare two prompt versions head-to-head, and on live production traces to catch quality drops the moment they happen. Convert any cluster of production failures into a dataset in one click — your test set evolves with reality, not against it.
When a judge gives a verdict, you can inspect why: every LLM-as-judge execution is itself traced, so you see the exact prompt the judge received and the reasoning it returned. Trust the scores, debug the bad ones, and audit the whole chain for compliance.
Capabilities
What you get
Three evaluator types
LLM-as-judge, deterministic / statistical, and human-in-the-loop
Custom evaluators in Python
Bring your own logic, wired into the same pipeline
Evaluate at any level
Session, trace, or span — score whole conversations or single tool calls
Side-by-side experiment runs
Compare prompt v1 vs v2 across thousands of cases, see win/loss/tie
Production traces → datasets in one click
Curate real failures into golden test sets, no synthetic data drift
Continuous online evaluations
Score live traffic as it flows, surface regressions in minutes
Eval-of-evals
Every judge run is itself traced, so you can audit and trust the scoring
Frequently Asked Questions
Everything You Need to Know About Evaluation
Get clear answers, fast — talk to our team and get tailored insights for your business in just a few minutes.
Can evaluations run automatically?
Yes — Auto-Evaluation runs Coherence, Factual Accuracy, and Toxicity on every incoming trace org-wide, until your configured usage limit.
Can I set up evaluations from my IDE?
Yes — via the Netra MCP server: browse and create evaluators, build datasets, and launch test runs directly from Cursor, Claude Code, Windsurf, and others.
Do you have a ready-made set of evaluators?
Yes. A curated evaluator library across Quality, Multimodal, Guardrails, Performance, Semantic, and Agentic categories, each pre-configured with a pass threshold (for example Answer Relevance ≥0.7, Toxicity ≥0.5). One-click add, no setup.
How do I evaluate my RAG agent?
Use the RAG-specific evaluators in the library — Context Precision, Context Recall, Context Relevance, Answer Correctness, Answer Relevance, Semantic Similarity, SQL Semantic Equivalence — or build a custom evaluator if none fit.
How do I test my chatbot or conversational agent?
Defining the expected output for every possible input is not practical for multi-turn agents. Use Simulation instead — define a goal, persona, and facts, and Netra runs realistic multi-turn conversations and scores them automatically.
What is online evaluation and why does it matter?
Online eval gives you live monitoring of user interactions and agent behavior as it happens — no synthetic data needed. Pull real production traces directly into a dataset and run evaluators against actual user interactions, closing the loop between what you tested and what is really happening in production.
Can I set alerts beyond evaluation?
Yes. Evaluations score quality on a dataset or trigger; Alerts monitor cost, latency, error rate, and anomalies in real time across all production traffic — so you are notified the moment something breaks, not just when you run an eval.
Can I create custom evaluators?
Yes. Two ways: LLM-as-Judge for subjective and semantic criteria such as correctness and relevance, or a Code Evaluator (JavaScript or Python) for deterministic checks like format validation, regex, and calculations.
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