What is chatbot testing?

Chatbot testing is the process of evaluating how an AI chatbot performs across real-world conversation scenarios. It includes checking for hallucinations, intent recognition accuracy, bias, conversation flow consistency, and compliance with safety guidelines. Automated chatbot testing uses AI evaluators to run thousands of scenarios and score every response without manual intervention. For the full methodology, read the chatbot testing guide.

What metrics are measured in chatbot QA?

TestMu AI measures 9 quality metrics: bias detection, hallucination detection, completeness, context awareness, response quality, conversation flow, user satisfaction, file handling quality, and file generation accuracy. Each metric has its own pass/fail threshold, a minimum score from 0 to 1 that the metric must reach to pass.

How do I connect my chatbot?

No SDK or code change is needed. Save an endpoint profile that tells TestMu AI how to reach your chat API over REST, WebSocket, Server-Sent Events, or sequence-indexed polling, including authentication and the request and response shape. A standard chat agent connects in under 30 minutes, and firewall-restricted endpoints can be reached through a secure tunnel.

Can chatbot testing catch prompt injection and jailbreaks?

Yes. Adversarial testing agents probe every run for prompt injection, data exfiltration, and harmful or toxic output. For a dedicated security pass, the testmu-a2a CLI runs a red-team evaluation across categories including prompt injection, jailbreak, PII leakage, hijacking, and policy violation, and returns a letter grade from A+ to F with a per-category breakdown. See AI red teaming for the full approach.

Can I test chatbots in languages other than English?

Yes. Scenario generation, execution, and evaluation are multilingual, with the language selected when scenarios are generated. Recently added chat languages include Tamil, Kannada, and Hebrew, with right-to-left rendering for Hebrew in both the scenario editor and the result viewer.

Can it check the facts my chatbot states against my own data?

Yes. Data Validation checks the values a chatbot states mid-conversation, such as an order status or a price, against a read-only endpoint on your own system of record, so a fluent but wrong answer fails. Each validated fact shows what the chatbot said, what your API returned, and the transcript quote as evidence.

What is the difference between chatbot testing and agent testing?

Chatbot testing focuses on validating conversation quality in chat-based and voice-based AI chatbots. Agent testing is broader and covers every conversational AI agent type, including phone and IVR agents, video agents, and image analyzers. Chatbot testing is a focused subset of agent testing, built on the same AI evaluator infrastructure.

Can I automate chatbot regression testing?

Yes. Schedule chatbot test runs using preset frequencies or full custom cron expressions with IANA timezone support, or trigger them from your CI/CD pipeline with the testmu-a2a CLI, which outputs JUnit XML for GitHub Actions, GitLab CI, Jenkins, and CircleCI. Regressions are caught on every build before deployment.

Chatbot Testing Platform: Detect Hallucinations

Q: How does automated chatbot testing work?

An AI evaluator agent is deployed to interact with your chatbot like a real user. It sends structured prompts across test scenarios, captures every response, and scores it on 9 quality metrics such as hallucination detection, bias, context awareness, and completeness, while specialized testing agents probe for toxic output, prompt injection, and failed handoffs to a human. Results are reported per scenario with confidence-weighted pass/fail verdicts.

Deploy autonomous AI evaluators to test your chatbots across thousands of conversation scenarios. Catch hallucinations, bias, and broken flows before real users do.

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Text and Voice Chatbot Testing on Generated Scenarios

Chatbot testing checks that an AI chatbot gives correct, grounded, and safe answers across real multi-turn conversations before customers see it. TestMu AI generates the scenarios from your documents, runs them against your chat or voice bot, and scores every reply on 9 quality metrics.

Text Chatbot Testing

Voice Chatbot Testing

Scenario Generation

Go-Live Assessment

Text Chatbot Testing

Score every chat conversation on 9 quality metrics, including hallucination detection, bias, context awareness, and conversation flow.

9 Quality Metrics

Score bias, hallucination, completeness, context awareness, response quality, and more.

Workflow-Based Test Generation

Auto-generate 60-100+ test scenarios from uploaded docs, PRDs, or connected Jira and Confluence.

Go-Live Assessment

Get a Green, Yellow, or Red production-readiness verdict before every chatbot deployment.

9 Chatbot Testing Metrics and Go-Live Assessment

Confidence by Evaluation Volume

HIGH (100+ evaluations), MEDIUM (50-99), LOW (20-49), VERY LOW (below 20). Confidence calibrates against evaluation volume.

9 Quality Metrics Across Every Chat Interaction

Bias, hallucination, completeness, context awareness, response quality, flow, user satisfaction, file handling, and file accuracy.

4-Dimension Go-Live Assessment

Each run scores Functional Completeness, Quality Standards, Risk Profile, and Operational Readiness, each weighted at 25%.

Pass/Fail Analysis Output

Pinpoint every match and discrepancy in chatbot responses, tracked as Pass, Fail, or Partial against your defined criteria.

Manage, Schedule, and Monitor Chatbot Tests

Project and Environment Management

Create chatbot test projects, manage environments, and scope variables with bulk creation support.

Test Profiles and Personas

Inject reusable test data and run scenarios across a pre-built or custom persona library for targeted chatbot evaluation.

Custom Validation Criteria

Define evidence-based pass/fail rules per chatbot scenario with High/Medium/Low confidence tracking.

Security and Infrastructure

Execute via HyperExecute with optional secure tunnels for firewall-restricted chatbot endpoints.

Scheduling Engine

Automate chatbot test runs using preset frequencies or full custom cron expressions with IANA timezone support.

Observability and Reporting

Monitor chatbot performance across test runs with unified dashboards, exportable reports, and real-time quality trends.

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Across the Chatbot Stack

Test Chatbots on Dialogflow, Rasa, Amazon Lex, and More

Your chatbot might run on Dialogflow, Rasa, a customer service platform, or your own LLM stack, and it rarely works alone. It hands off to voice and phone agents and shares intents with them. Find platform-specific guides, adjacent agent surfaces, and deeper reading below.

Chatbot Platforms

Customer Service AI

Quality and Safety

Adjacent Agent Surfaces

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