Can LLMs (Finally) Solve Everyday Problems?
Towards Data Science
Your home for data science & AI. A publication for data science and artificial intelligence professionals.
Large language models power more and more tools we encounter in our daily lives, but their growing footprint has often come with clear downsides—from dead-end chats with AI support agents to questionable (if not downright dangerous) advice.
In this week's edition of The Variable, we zoom in on three use cases where the gap between LLMs' potential and the results they produce appears to have significantly shrunk. From fashion to document ingestion, we look at the inner workings of models in action, and learn about the work it takes to harness their power effectively. Let's dive in!
Overcome Failing Document Ingestion & RAG Strategies with Agentic Knowledge Distillation
Tula Masterman and her team tackle a common problem for practitioners across industries: low rates of success in ingesting documents using machine learning techniques. Learn about their innovative solution, which combines an agentic approach with a multi-tiered pyramid of information.
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1 周As a data scientist, how can integrating agentic knowledge distillation enhance the efficiency and accuracy of document ingestion in Retrieval-Augmented Generation (RAG) models, and what key factors should be considered when implementing this approach?
Senior Data Scientist | Tech Leader | ML, AI & Predictive Analytics | NLP Explorer
1 周The progress in overcoming document ingestion challenges with LLMs is exciting! Agentic Knowledge Distillation seems like a structured way to improve retrieval, but I wonder—how does it handle scalability as document volumes grow? I've been exploring LLM-powered solutions in applied NLP, and one recurring challenge is balancing precision and efficiency in RAG workflows. Would love to hear thoughts on how others are addressing hallucinations and factual consistency in large-scale document retrieval!
EAM & Information Management Specialist
1 周The article on Agentic Knowledge Distillation + Pyramid Search Approach presents an innovative way to improve document ingestion for Retrieval-Augmented Generation (RAG) systems. By structuring knowledge in a multi-tiered pyramid, this method enhances retrieval efficiency, reduces token usage, and enables deeper cross-document analysis. However, there are key areas for further refinement: * Evaluation Metrics – More benchmarking against existing RAG solutions is needed. * Implementation Depth – Adding sample code or real-world deployment insights would improve applicability. * Comparison with Alternatives – A side-by-side comparison with other retrieval methods would add clarity. * Handling Dynamic Data – Strategies for adapting to continuously changing datasets could be explored further. * Broader Use Cases – Expanding beyond finance to domains like healthcare, legal, and research could showcase versatility. This approach has great potential to reshape how AI systems process and synthesize large-scale data. Looking forward to seeing how it evolves! Would love to hear thoughts from the community—how do you see this fitting into your AI workflows??
Principal Data Scientist, Applied AI Practice Lead
1 周Looking forward to reading The Variable in its new home! Thanks for featuring my post in this edition!