Measuring What Matters in GenAI

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The field of Generative AI has seen rapid advancements, yet the challenge remains in effectively measuring and validating these systems’ outputs. This session provides a comprehensive overview of the evaluation techniques that are pivotal for Generative AI systems, particularly those involving retrieval-augmented generation (RAG). We will dive into the intricacies of retrieval evaluation, discussing key metrics that help assess the relevance and accuracy of information retrieved by AI. Following this, we transition into evaluating generative aspects, exploring how these metrics ensure the generated content meets the desired standards of coherence and relevance.

Key Takeaways:

  • Understanding the foundations of RAG and its significance in enhancing the capabilities of generative AI models.
  • Practical knowledge on acquiring and creating datasets for evaluation.
  • Insights into various retrieval evaluation metrics.
  • Exploration of generation evaluation metrics and evolving approaches to evaluate the quality of AI-generated content.
  • Practical knowledge on implementing these metrics in real-world AI applications to ensure robust and reliable outputs.

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