A chatbot speaks for your business. We test what it says.
LLM chatbots can answer fluently and still be wrong, unsafe or off-brand. We test accuracy, safety, tone and multi-turn behaviour with realistic and adversarial conversations, and QA engineers judge the outputs that a score cannot.
Overview
What it is. LLM and chatbot testing evaluates conversational assistants built on large language models: customer support bots, internal assistants, knowledge-base search and domain copilots. We test single answers and whole conversations, including how the bot uses your knowledge sources, handles requests it should refuse and hands over to a person.
Who it is for. Teams about to launch a chatbot, teams changing the model, prompt or knowledge base behind one, and organisations whose assistant talks to customers or staff.
What you get. Findings grouped by accuracy, safety and tone with the conversations that show them, a reusable set of test conversations, and a clear account of what was and was not covered.
Why it matters
The problem it addresses. A fluent answer is not necessarily a correct one. Chatbots can state invented facts, follow instructions hidden in user text, lose context over a long conversation, or reply in a tone your business would never choose.
When it matters most. Before launch, when the knowledge base, prompt or model changes, when the bot can take actions, and when its answers carry legal, financial or safety weight.
What happens without it. Customers receive wrong or unsuitable answers, the first sign of a problem is a screenshot, and nobody can show how the bot was checked.
What we test
Accuracy and hallucination
whether answers are correct, complete and supported by the knowledge sources, and how the bot behaves when it does not know
Safety and refusal
harmful, biased or out-of-scope requests, and whether the bot declines appropriately
Prompt injection and jailbreaking
attempts to override instructions or extract hidden prompts, including through pasted content
Tone and brand voice
consistency of style, politeness and wording against your guidelines
Multi-turn conversation
memory, context handling, topic changes and contradictions across a long exchange
Knowledge grounding
behaviour with missing, outdated or conflicting sources in the knowledge base
Escalation and fallback
handover to a person, error messages and behaviour when the model or a tool fails
Language and format
behaviour across the languages and input styles your users actually use
How UpDoer approaches LLM & Chatbot Testing
Understand
Requirements, tickets, code diffs and product context.
We review the bot’s purpose, audience, prompts, knowledge sources and any actions it can take, and agree what correct, acceptable and unacceptable replies look like.
Risk Mapping
Where a failure would cost the most.
Conversation topics are ranked by consequence: advice users act on, anything involving money or personal data, and requests the bot must refuse.
Test Strategy
Coverage chosen, test cases drafted.
We design test conversations: typical, difficult and adversarial, single-turn and multi-turn, and decide what is re-run automatically after every change.
Execute
Manual and automated testing runs.
Test conversations run against agreed environments. Exploratory sessions try what scripts miss: interruptions, topic switches, vague questions and pushing back.
AI-Assisted Quality Engineering
Failures classified, release risk scored.
AI helps draft question variations and adversarial prompts, group similar bad answers and give each reply a first-pass score against a rubric.
Human Verification
A QA engineer checks every flagged result.
A QA engineer reads the conversations behind every flagged item, judges accuracy, safety and tone in context, and confirms which are real defects.
Report & Improve
The verified quality signal, then better coverage.
Findings are reported with the conversations that show them and the coverage limits. Confirmed failures join the regression set run after each model, prompt or knowledge-base change.
LLM & Chatbot Testing: from conversation risk to release signal
01
Where it breaks
Invented factsPrompt injectionOff-brand tone02
Test workflow
AccuracySafetyMulti-turnFallback03
AI assists
Draft adversarial promptsGroup bad answersScore replies first-pass04
Humans decide
Judge accuracy and toneRate real exposure05
Release signal
Coverage by riskOpen findingsGo / no-go by a person
Illustrative workflow. Not a client result or a measured outcome.
LLM and chatbot coverage matrix
| Quality area | What we validate | AI assistance | Human validation |
|---|---|---|---|
| Accuracy | Correct, complete answers supported by the knowledge sources | Drafts question variants, flags unsupported statements | Judges correctness against policy and product facts |
| Safety and refusal | Harmful, biased or out-of-scope requests | Proposes adversarial and boundary prompts | Assesses real-world severity |
| Prompt injection | Instruction overrides and prompt extraction attempts | Generates injection variants | Decides whether an exposure is meaningful |
| Tone and voice | Style and wording against brand guidelines | Flags replies that depart from the style guide | Judges what is acceptable for the audience |
| Multi-turn behaviour | Memory, context shifts and contradictions | Builds long conversation scripts | Reviews coherence across the whole exchange |
| Fallback and escalation | Handover, errors and tool failures | Suggests failure scenarios | Verifies the customer experience when things break |
Quality area: Accuracy
- WHAT WE VALIDATE
- Correct, complete answers supported by the knowledge sources
- AI ASSISTANCE
- Drafts question variants, flags unsupported statements
- HUMAN VALIDATION
- Judges correctness against policy and product facts
Quality area: Safety and refusal
- WHAT WE VALIDATE
- Harmful, biased or out-of-scope requests
- AI ASSISTANCE
- Proposes adversarial and boundary prompts
- HUMAN VALIDATION
- Assesses real-world severity
Quality area: Prompt injection
- WHAT WE VALIDATE
- Instruction overrides and prompt extraction attempts
- AI ASSISTANCE
- Generates injection variants
- HUMAN VALIDATION
- Decides whether an exposure is meaningful
Quality area: Tone and voice
- WHAT WE VALIDATE
- Style and wording against brand guidelines
- AI ASSISTANCE
- Flags replies that depart from the style guide
- HUMAN VALIDATION
- Judges what is acceptable for the audience
Quality area: Multi-turn behaviour
- WHAT WE VALIDATE
- Memory, context shifts and contradictions
- AI ASSISTANCE
- Builds long conversation scripts
- HUMAN VALIDATION
- Reviews coherence across the whole exchange
Quality area: Fallback and escalation
- WHAT WE VALIDATE
- Handover, errors and tool failures
- AI ASSISTANCE
- Suggests failure scenarios
- HUMAN VALIDATION
- Verifies the customer experience when things break
Where AI helps
- Drafting question variations, paraphrases and adversarial prompts from a topic list, for review before use.
- Grouping similar wrong or unsafe replies by cause, such as a missing source or a prompt weakness.
- Giving replies a first-pass score against a rubric so reviewers start with the likeliest problems, never as the final judgement.
- Summarising a test run into a readable risk note that a QA engineer verifies.
How we use AI: AI can accelerate test design, analysis and repetitive QA work, but important outputs remain subject to human review and validation. It informs the release decision; it does not make it.
Where humans matter
- Judging whether an answer is right for your product, customers and policies.
- Assessing tone, fairness and potential harm, which depend on context.
- Deciding whether a reply to an adversarial prompt is a real exposure or acceptable behaviour.
- Confirming that a failure is reproducible and not a one-off in a probabilistic system.
- Owning the release recommendation and stating what the testing did not cover.
Tools we build with
- Automation
- SeleniumPlaywrightAppiumKatalonTestComplete
- API & Performance
- PostmanSoapUISwaggerApache JMeter
- CI/CD & Reporting
- JenkinsTeamCityAllureMaven
Frequently asked questions
How do you test for hallucinations?
We run questions with known answers, questions the knowledge base cannot answer and questions with conflicting sources, then check that replies are supported by the sources and that the bot says when it does not know. A QA engineer reviews every flagged reply.
Can you test for prompt injection?
Yes. We try instruction overrides, requests to reveal hidden prompts and hostile content pasted into the conversation, and report which ones succeed and how serious each exposure is. This is QA-level testing, not a formal penetration test.
Do you need access to the model or only the chat interface?
The chat interface is enough to start. Access to prompts, knowledge sources and logs lets us find causes rather than only symptoms, and we would agree that access with you.
What happens after we change the model or prompt?
We re-run the saved test conversations and compare the results with the previous run, so you can see what changed and decide whether it is acceptable.