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AI Validation Methodology: Testing & Quality Assurance for Chatbot & AI Agents

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– 01 AI Validation Methodology Testing & Quality Assurance for Chatbot & AI Agents

Service Description

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.
Expected results: 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: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.
Target:Manufacturing & Automotive
  • Organisation: REPLY
  • Type: experiment
  • SUT/E: Chatbot & AI Agents
  • Average duration: 4-6 months
  • Contact: m.diflorio@reply.it

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