To address the limitations of LLMs, the article suggests the need for a "RAW" equivalent in AI, where accuracy is critical. The RAG (Retrieval-Augmented Generation) pattern is proposed as a solution, combining LLMs with a true knowledge base to provide accurate and up-to-date information. This approach is seen as essential, especially for real-time data, as retraining models is costly and time-consuming. The article concludes that while LLMs are efficient and widely used, they may not always provide the precision required in certain scenarios, highlighting the importance of augmenting them with reliable data sources.
Key takeaways:
- In the early days of digital design, BMP files were used for lossless image storage, but the need for more efficient formats led to the adoption of JPEG.
- Large language models (LLMs) are compared to JPEGs as they balance accuracy and efficiency, resulting in potential errors or "hallucinations."
- The RAG (Retrieval-Augmented Generation) pattern is a solution to enhance LLMs by pairing them with a true knowledge base for accurate information.
- There is a possibility that AI will develop a "RAW" equivalent for knowledge, similar to lossless formats in image and audio, to ensure accuracy in critical scenarios.