Built-in templates for hallucination, toxicity, correctness, and bias evaluations. Paste prompt + LLM response for instant scoring — no login, no install.
HallucinationToxicityCorrectnessBias4 TemplatesFree · No login
LLM Output Evaluator
Template:
// Prompt + LLM Response0 chars
// Evaluation Results
▤Paste prompt + response and click Evaluate
Template: Hallucination
Top tools to test LLM safety & toxicity
These are the most widely used tools for testing safety and toxicity of LLM outputs in 2025-2026, covering open source libraries, managed APIs, and evaluation frameworks with built-in templates for AI safety assessment.
Tool
Type & focus
Toxicity
Halluc.
Free
This tool
Browser · No install · Evaluation templates
Yes
Yes
Yes
DeepEval
Python · Open source · LLM-as-judge
Yes
Yes
OSS
PromptFoo
CLI · Red-teaming · Safety benchmarks
Yes
Partial
OSS
Giskard
Python · Bias · Injection · Safety
Yes
Yes
Freemium
Azure AI Content Safety
Cloud API · Managed · Enterprise
Yes
No
Pay/use
Arize Phoenix
LLM observability · Tracing · Eval
Partial
Yes
Freemium
AWS Guardrails
Bedrock · Managed filter · PII
Yes
No
Pay/use
Evaluation dimensions explained
Hallucination Detection in AI Safety
Hallucination occurs when an LLM generates content that is factually incorrect, fabricated, or not grounded in the provided context. Key signals: specific named entities (people, dates, figures) that are unverifiable, confident claims about niche topics, internal contradictions between different parts of the response, and mismatch between the prompt's context document and the response's claims. Hedging language (I believe, might be, I think) is a positive signal — it indicates the model is acknowledging uncertainty rather than hallucinating confidently. Using hallucination detection ai tools helps identify these patterns at scale.
Toxicity Assessment
Toxicity covers harmful, offensive, or dangerous output: hate speech targeting protected groups, threats or incitement to violence, sexual content including minors, detailed instructions for illegal activities, and harassment. Modern LLMs have strong safety training but can be bypassed by jailbreaks, roleplay framing, or indirect elicitation. Toxicity testing should cover direct prompts AND adversarial/indirect framings. Azure AI Content Safety and AWS Bedrock Guardrails are the main managed APIs for production-grade toxicity filtering.
Correctness Evaluation
Correctness evaluates factual accuracy and task completion quality. For code generation: does the code run and produce the expected output? For factual Q&A: are claims verifiable? For summarization: does the summary capture key information without distortion? Correctness evaluation typically requires a reference answer or ground truth. G-Eval and LLM-as-judge approaches use a scoring model to compare the output against a reference without exact string matching.
Bias Detection in LLM Outputs
Bias in LLM outputs includes demographic stereotyping (associating groups with negative traits), differential advice quality based on names or demographic markers in prompts, underrepresentation or erasure, and unfair sentiment polarity across groups. Bias evaluation requires counterfactual testing — running the same prompt with different demographic markers and comparing responses. DeepEval and Giskard include bias detectors that run multiple prompt variants automatically.
FAQ — LLM safety evaluation
The leading tools in 2025-2026: DeepEval (Python, open source, best for hallucination + toxicity with LLM-as-judge). PromptFoo (CLI-based red-teaming and safety benchmarks). Giskard (bias, injection, and safety testing with visual reports). Azure AI Content Safety and AWS Bedrock Guardrails for managed production-grade filtering. This evaluator above for quick browser-based testing with no install.
Key approaches: (1) Reference grounding — compare the response against a source document and flag unsupported claims. (2) Consistency testing — ask the same question in multiple ways and detect contradictions. (3) Confidence signals — hedging phrases indicate uncertainty, while confident claims about niche topics are high risk. (4) Named entity verification — specific people, dates, and citations are the most hallucination-prone. (5) LLM-as-judge — use a second model (GPT-4, Claude) to score factual accuracy against a reference. DeepEval's HallucinationMetric and Arize Phoenix implement option 5.
A complete LLM evaluator should include: Hallucination (factual accuracy, grounding score), Toxicity (harmful content categories), Correctness (task completion, accuracy vs reference), Bias (demographic differential treatment), Coherence (logical consistency, fluency), Relevance (answer-to-question alignment), and Safety (jailbreak resistance, PII leakage). Built-in templates for each dimension accelerate evaluation without manual prompt engineering.
LLM-as-judge uses a powerful model (usually GPT-4, Claude 3.5 Sonnet, or Gemini Pro) to evaluate the outputs of a smaller or different model. The judge model receives a scoring prompt with criteria (hallucination, toxicity, relevance) and rates the output on a 1-5 or 0-1 scale. Advantages: handles nuanced quality judgments without exact reference matching. Disadvantages: introduces bias from the judge model's own preferences, and is expensive at scale. Tools like DeepEval, Arize, and G-Eval implement this pattern. The judge model must be more capable than the model being evaluated.
LLM evaluation templates provide pre-built rubrics and scoring criteria for assessing AI outputs without manual configuration. Common templates cover hallucination detection (factual accuracy), toxicity assessment (harmful content), correctness scoring (task completion), and bias evaluation (group fairness). Templates accelerate LLM safety testing and standardize metrics across different evaluations, making them essential for teams auditing AI responses at scale.
Yes. This tool provides a browser-based DeepEval alternative with no installation required — just paste your prompt and LLM response to evaluate hallucination, toxicity, correctness, and bias instantly. It's free, no login needed, and uses built-in evaluation templates similar to DeepEval's LLM-as-judge approach, making it ideal for quick AI safety evaluations without Python setup or API costs.