Large language models like ChatGPT have the potential to transform various fields but integrating them into real-world products poses challenges. A powerful strategy called retrieval-augmented generation (RAG) has emerged, allowing connection to external information sources for more accurate outputs. Several articles explore the intricacies and practical considerations of working with RAG, helpful for those in machine learning or data science. Other topics covered include NaN values in Python, dimensionality, counterfactuals, generative-AI, reinforcement learning for dynamic pricing, and leveraging pre-trained models for a custom AI weather-forecast app.
Unlocking the Potential of Retrieval-Augmented Generation (RAG) for Your Business
When large language models (LLMs) first emerged, they promised to revolutionize various industries and professions. However, as we have gained more experience with them, we have become aware of their limitations and the challenges of integrating them into real-world products. To overcome these challenges, retrieval-augmented generation (RAG) has emerged as a powerful strategy.
RAG allows you to connect pre-trained models with external, up-to-date information sources, resulting in more accurate and useful outputs. In this collection of articles, we explore the intricacies and practical considerations of working with RAG. Whether you are a data scientist, product manager, or deeply involved in machine learning, understanding RAG can help you prepare for the future of AI tools.
Add Your Own Data to an LLM Using Retrieval-Augmented Generation (RAG)
If you are new to RAG, Beatriz Stollnitz provides a beginner-friendly introduction. This resource explains the theoretical foundations of RAG and offers a hands-on implementation guide. Learn how to create a chatbot that helps customers find information about your company’s products.
10 Ways to Improve the Performance of Retrieval Augmented Generation Systems
If you have already started experimenting with RAG, you may have encountered challenges in making it consistently produce the desired results. Matt Ambrogi’s guide offers practical insights into improving the performance of RAG systems. Learn how to bridge the gap between RAG’s potential and tangible benefits.
RAG vs Finetuning — Which Is the Best Tool to Boost Your LLM Application?
While RAG is a powerful strategy, it is not the only option for enhancing AI products. Heiko Hotz compares RAG with model fine-tuning, another popular approach for improving the performance of generic LLMs. Discover the strengths and weaknesses of each method and learn how to align the optimization method with your specific task requirements.
These articles provide valuable insights into leveraging RAG and other AI strategies. By understanding these approaches, you can redefine your company’s way of work and stay competitive in the ever-evolving AI landscape.
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