We taught a small LLM to throw away 68% of our RAG context

In a recent blog post, the author discusses how they taught a small language model (LLM) to effectively discard 68% of their retrieval-augmented generation (RAG) context. This approach aims to enhance the efficiency of the model by focusing on the most relevant information while minimizing unnecessary data. For more details, you can read the full article here: We taught a small LLM to throw away 68% of our RAG context.