Advanced Testing Methodology for solutions based on Chatbots and AI Agents, ensuring accurate, secure, and hallucination-free responses. The service transforms the inherent uncertainty of language models into measurable performance, validating both the quality of information retrieval and the consistency of response generation. Through real-world usage scenarios, agents operate within defined boundaries, reducing both reputational and technical risks.
Significant reduction of AI hallucinations, improved data retrieval accuracy, validation of groundedness (adherence to factual information), and increased reliability of agents in multi-step tasks.
Methodology
KPI Definition: Identification of domain-specific success metrics (e.g., Faithfulness, Answer Relevance, Context Precision).
Gold Standard Dataset: Creation of a “ground truth” test set (question/context/answer) for objective benchmarking.
Retrieval Evaluation: Testing the effectiveness of the vector database and chunking strategy to ensure the AI consistently retrieves the correct information.
Agentic Logic Testing: Verification of the agents’ ability to plan and execute complex tasks using external tools (APIs, databases).
Adversarial Testing (Red Teaming): Simulation of hostile or ambiguous inputs to test the system’s robustness and security.
Manufacturing & Automotive