AI services
Chatbot Testing & Evaluation
Chatbot testing services for teams preparing a release or model change. I turn the questions and evidence your team approves into repeatable checks, a prioritized defect report and a regression runner you can keep using.
Discuss this serviceIs this the right service?
Who it is for. Product and engineering owners with an existing document chatbot and a release decision to make.
The problem. A convincing demo does not show which answers fail, what changed between versions or whether a fix holds.
The result. A documented release decision based on agreed checks, known limitations and reviewable evidence.
One existing document assistant and one release decision. Your team owns acceptance criteria; I turn approved examples into executable checks and reviewable defects.
What it covers
Acceptance questions and expected evidence
Start with the decisions the assistant supports, its users and the document collection it can consult. Domain experts decide what counts as a supported answer; a generated reference answer is not the acceptance authority. Include frequent questions and consequential edge cases.
- Build a question set with approved source passages and scoring rules.
- Record expected refusals, escalation paths and unresolved judgments.
Grounding, citations and completeness
Check whether an answer follows its retrieved evidence, cites the relevant source and covers the required points. Separate retrieval failures from answer-generation failures so engineers know which part to change. An evaluation result applies to the tested cases and configuration.
- Trace failed answers to retrieved passages and source versions.
- Produce a defect record for unsupported statements, missing evidence and incomplete answers.
Multi-turn conversation failures
Exercise follow-up questions, ambiguous requests and changing context. A useful response may be a clarification or refusal rather than a confident answer. Agree those behaviors with the product owner before scoring the conversation.
- Write conversation scenarios with explicit checkpoints.
- Capture transcripts showing context loss, ambiguity and refusal failures.
Tool-call error cases
Where the assistant calls tools, include invalid arguments, permission failures and unavailable upstream systems. Scope these checks around the assistant’s observable behavior and approved test access; deeper integration repairs need their own boundary.
- Define expected responses to rejected or failed tool calls.
- Capture reproducible error cases without exposing production credentials.
Repeatable regression execution
Conversational AI testing services need a way to compare changes without relying on a single attractive response. Pin the tested configuration and preserve run inputs. For variable outputs, agree repeated runs and human review rules rather than pretending every response is deterministic.
- Deliver an executable runner with configuration and scoring instructions.
- Record versioned results for prompt, model or retrieval changes.
Prioritized defects and remediation
Review failures by consequence and likely cause with your engineers. Agree which fixes belong in this engagement and which require a separate build. Re-run the relevant cases after changes and retain unresolved limitations in the handover.
- Deliver a prioritized issue list with reproduction steps.
- Document agreed fixes, retest results and remaining release decisions.
Approach
- 01
Agree the acceptance set
Review one existing assistant, its intended users, approved evidence and access. Your domain owners approve expected behavior and scoring before execution.
- 02
Run and investigate
Exercise questions, conversations and applicable tool failures. Inspect the evidence behind failed checks and distinguish retrieval, generation and integration causes.
- 03
Retest and hand over
Apply agreed fixes, rerun affected cases and review unresolved defects with the release owner. Hand over the runner and instructions so your team can repeat the checks.
What you get
An approved acceptance set, repeatable evaluation runner, failure report and agreed remediation handover.
- Receive the approved dataset, scoring rubric and source references in the agreed repository.
- Run the evaluation with documented configuration, dependencies and result interpretation.
- Review a prioritized defect report, agreed fixes and known limitations with your release owner.
- Supply permitted test access and a domain reviewer. Security attestation, penetration testing and voice/contact-center QA are outside this offer.
Tools and standards
I work with the existing assistant and its available interfaces. The runner can combine deterministic checks with reviewed model-based judgments where appropriate. We agree data handling and execution access before running client material; model-provider choice follows your constraints.
Relevant experience
At the United Nations, I built completeness, source-consistency and structural checks into a LangGraph report-drafting and revision workflow. This evaluation engineering experience informs how I design checks for your existing assistant.
Frequently asked questions
How do you test a chatbot?
We start with representative questions and approved evidence, then agree checks for grounding, citations, completeness and expected refusals. Conversation scenarios and relevant tool failures extend that set. Runs produce traceable failures for investigation and retesting. Your team reviews the results against its release criteria; a passing sample does not guarantee every future answer.
Can you evaluate a chatbot we already have?
Yes. This engagement starts with an existing document assistant. We first inspect its interface, retrieval visibility and available test environment to establish what can be observed. Your team supplies permitted access and representative questions. If the system cannot expose enough evidence, the scope identifies that limitation before making claims about answer quality.
Who defines the expected answers?
Your domain experts approve the evidence, required points and acceptable refusal or escalation behavior. I help turn those decisions into executable checks and a review rubric. Where more than one answer is acceptable, we score the agreed properties rather than exact wording. Unresolved domain questions remain decisions for your owners.
Does the engagement include fixes?
The scope names the remediation work included after evaluation. Small prompt, retrieval or validation changes may be agreed alongside the test run; a new assistant or substantial connector rebuild needs a separate scope. The handover records fixes, retest results and open defects so the release owner can decide what to accept.
Related services
If you need to build the assistant before evaluating a release, start with private knowledge assistant implementation.
If failures originate at a business tool boundary, scope the repair through agent API integration.
For a wider drafting, review and revision workflow, explore agentic automation.
Working together
Projects are delivered remotely, with a defined scope, scheduled reviews and written progress updates. We agree meeting times, access requirements and acceptance criteria before work begins. Code, documentation and handover are included as specified in your engagement.
Discuss this engagement
Bring one existing assistant, a representative question and the release decision you need to make.
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