RAG Workshop Experience: From Hallucinations to Real-World Applications Last week, I had the opportunity to attend a workshop on Retrieval Augmented Generation (RAG) at Northeast University, led by Toby Tobkin. Toby did an excellent job of presenting this complex topic in a clear and engaging way. It was a whirlwind of information, but I came away with a solid understanding of RAG’s potential and the complexities involved in bringing it to life.
The workshop started by looking back at the history of technology and how different technologies have gone through adoption waves. It was fascinating to see how the current wave of AI and LLMs fits into this historical context.
Then, the focus shifted to the limitations of LLMs: hallucinations, insufficient knowledge, and opaque reasoning. That’s where RAG comes in! The workshop stressed that while RAG seems simple in principle, building a robust RAG system is a lot more complicated than it appears. It’s a continuous process of maintenance and optimization.
We explored diverse use cases for RAG, from document question-answering to structured data extraction, search, and personalized assistants. The examples showcased the potential of RAG across various industries, including legal research, regulatory compliance, and retail automation.
The workshop went deep into the architecture of RAG systems, explaining the key components like indexing, retrieval, and generation. We learned about different data scraping techniques, embedding models, and fine-tuning strategies. It was fascinating to see how the different components work together to create a powerful RAG system.
A crucial aspect of RAG is reranking, which improves the relevance of retrieved information. The workshop compared various reranking approaches and explained their strengths and weaknesses. We also discussed the possibility of fine-tuning the generator to enhance RAG’s accuracy.
The workshop then transitioned into the practical considerations of building RAG systems for production environments. We talked about the differences between PoCs and real-world applications, emphasizing the need for robust data gathering, cost-effective inference, reliable monitoring, and robust security measures.
We also discussed the importance of evaluating RAG systems effectively. The workshop introduced basic evaluation metrics like precision, recall, and weighted F1 score, and presented more advanced concepts like CRAG (Comprehensive RAG Benchmark). We explored common failure modes in RAG systems, including indexing, retrieval, and generator issues.
The workshop concluded with a practical programming kickoff guide and a detailed repository structure for beginners to get started with RAG development. I’m excited to apply my newly acquired knowledge and build a RAG system of my own.
Overall, the workshop was a valuable learning experience. It provided a solid foundation in RAG and highlighted the potential challenges and rewards of building these systems. I’m eager to see how RAG technology continues to evolve and shape the future of AI.
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