How QualiZeal Elevated GenAI Quality Engineering for a Leading Insurance Company.
Location
US
Industry
Insurance
Year
2025
Overview
Our client, a leading insurance company (name withheld for confidentiality), has been pioneering internal and external chatbot development initiatives leveraging Generative AI (GenAI). Their GenAI-powered chatbots were designed to support user queries, workflows, and task automation at scale. However, traditional QA methods fell short in ensuring the reliability, safety, and compliance of LLM-driven systems.
Challenges
Issues
Direct Business Challenge
Superficial Testing / Limited Scope
Testing was restricted to UI or functional validations only, with no coverage of prompt logic, LLM internals, or agent interactions.
High Manual Effort & SME Overload
Generative AI outputs risk hallucination, biased content, policy violations, or exposure of sensitive data (PII).
Hallucinations, Bias & Safety Risks
Generative AI outputs risk hallucination, biased content, policy violations, or exposure of sensitive data (PII).
No Lifecycle & Contextual Testing
Lack of tests for context-switching, multi-turn conversations, agent chaining, or evolving state across sessions.
No Feedback Loop from Production
Failures or anomalies in production (real user interactions) weren’t fed back for refinement of test scenarios.
Lack of Traceability & Version Control
Test assets (prompts, guardrails, test cases) evolved without versioning, making it hard to audit or roll back.
Qualizeal’s strategic & tactical solutions
Impact
Strategic & Tactical Solutions
Solution 1
Introduce multi-layer testing — validate not just UI and API, but also LLM behavior, chain-of-thought reasoning, prompt variants, and agent orchestration.
Solution 2
Deploy TestGen AI to auto-generate domain-specific prompts and test cases that reduce manual creation. Use reusable prompt/test libraries and templates to streamline SME effort.
Solution 3
Embed guardrails and safety checks — auto-validation for PII exposure, offensive content filters, bias/fairness screening, compliance rules (e.g. GDPR). Also run adversarial and negative prompt tests.
Solution 4
Build agent orchestration & lifecycle frameworks (e.g., based on Strands Agent) to simulate multi-agent flows, conversation drift, context jumps, fallback logic, and session continuity.
Solution 5
Implement logging & monitoring (via MongoDB / AEM / production logs) to capture real user prompts and responses, and integrate them into the test corpus continuously.
Solution 6
Introduce version control, traceability frameworks, and audit trails for all test artifacts. Maintain lineage from prompt → test case → result → model version.
Value Delivered
Test automation coverage, up from 40–50%
0
%
Reduction in SME manual validation effort.
40-
0
%
LLM/Agent
Testing depth expanded from UI-only to full LLM behavior and agent lifecycle validation.
