Adi Polak, addressing generative AI challenges at QCon London 2025, emphasized how achieving precision in data retrieval is essential for operationalizing AI effectively. She discussed the limitations of current Retrieval-Augmented Generation (RAG) systems and introduced the agenticRAG approach as a solution for enhanced accuracy. Drawing on real-world examples, Polak illustrated the impact of precision on customer trust and business outcomes as companies move from prototypes to production. She also noted the ongoing complexities of measuring precision in generative AI tasks and outlined both RAG and domain-specific fine-tuning as optimization strategies.
Achieving precision is one of the hardest things we need to do to operationalize AI, go from zero to one, from MVPs of prototypes to production, and see things that work.
The challenge with term search is that it often leads to outdated information and fails in handling ambiguous queries, which ultimately affects the user experience.
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