By Marcello Politi, Machine Learning Scientist at Pi School
The recent updates from the OpenAI developers conference have brought an exciting focus on expanding model context, a game changer in how we input and interact with AI models and, as a machine learning scientist, I am always eager to share new developments in AI.
In the world of AI, context is crucial. It defines the extent of tokens (or characters, if you prefer) we can input into a model simultaneously. Until recently, context limitations have been a hurdle, restricting our ability to craft prompts as comprehensively as we might like. But the landscape is changing. OpenAI’s latest models now boast a context length equivalent to a staggering 300-page book!
While this expansion is impressive, practical applications often reveal a different story. As the context window broadens, a decline in model performance is commonly observed. This presents a paradox: more isn’t always better in AI contexts.
This is where RAG, or Retrieval Augmented Generation, steps in. RAG enhances the input by adding relevant information from a database, providing a richer yet concise context. This method improves the model’s understanding and performance without dramatically increasing the input size.
For businesses, RAG represents a strategic advantage. It allows them to bypass the higher costs associated with larger OpenAI models while reaping the benefits of advanced AI capabilities.
At Pi School, we’re not just passive observers of these developments. We actively integrate methods like RAG into our challenges, customizing solutions for each customer. Our approach is to stay abreast of the latest AI trends without succumbing to them blindly. We believe in adapting these innovations purposefully and strategically to meet the diverse challenges our clients face.
The realm of AI models is vast and ever-evolving. As we continue to explore innovative frameworks like RAG, we open up new possibilities for optimizing performance and finding tailored solutions.