HALLUCINATION TESTING IN CHATBOTS
Hallucination Testing in Chatbots
Content
Project Description
AI/LLM
AI/LLM
AI/LLM
AI/LLM
Overview
This case study demonstrates how Tesvan implemented Hallucination Testing in Chatbots to ensure consistent, factual, and trustworthy AI interactions. Chatbots powered by large language models (LLMs) often generate hallucinations—plausible but incorrect or fabricated answers—that can damage user trust and business credibility.
To address this, Tesvan applied LLM-as-a-judge validation combined with reproducibility checks. This approach evaluates responses against domain-specific truth sources and verifies that the same input consistently yields the same factual output. The result is a chatbot that not only feels conversationally natural but also maintains reliability across diverse business scenarios.
Objective
- Detect and reduce hallucinations in chatbot responses.
- Ensure consistent outputs with reproducibility checks.
- Maintain trustworthy, factual communication with users.
- Apply LLM-as-a-judge validation for scalable quality control.
- Improve end-user trust and engagement.
Challenge
- LLMs generating fabricated or misleading answers.
- Difficulty in measuring factuality at scale.
- Inconsistent outputs for identical queries.
- Lack of robust validation methods tailored to enterprise use.
- Risk of user distrust due to unreliable responses.
Solution
- Deployed LLM-as-a-judge validation to automatically assess chatbot responses against factual ground truth.
- Implemented reproducibility checks to ensure consistent outputs across repeated inputs.
- Built a modular hallucination testing framework integrated into the QA pipeline.
- Designed feedback loops to retrain and fine-tune models based on detected hallucinations.
- Delivered trustworthy chatbot systems ready for enterprise deployment.
Result
By applying Hallucination Testing in Chatbots with LLM-as-a-judge and reproducibility techniques, Tesvan significantly improved chatbot reliability and reduced risks:
92%
reduction in hallucinations
97%
consistency rate across repeated queries
85%
increase in user trust scores
50%
fewer escalations to human agents
35%
faster validation cycles
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Testimonials
Aleksey Kudrya
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Aleksey Kudrya
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