Building Self-Evaluating OpenAI Agents for Profiles & Documents

About this session

The talk will demonstrate how an OpenAI-powered AI agent can read PDFs and enterprise documents and answer user questions with verifiable, source-grounded accuracy. As organizations struggle to extract insights from large volumes of legal, financial, and recruitment documents, this session will address the practical challenges of hallucinations, scalability, and trust in document-centric AI systems. The talk will include production-grade design patterns such as document ingestion pipelines, chunking and embeddings, retrieval-augmented generation (RAG), and response constraints using an application-level OpenAI API key. Attendees will learn how to implement evaluation controls, grounding strategies, and failure handling to ensure reliable, auditable answers in enterprise environments. This session is designed for practitioners who want to move beyond demos and deploy responsible, scalable document Q&A agents that deliver measurable business value.