Will Retrieval Augmented Generation (RAG) Be Killed by Long-Context LLMs?

Google's Gemini 1.5, an LLM capable of handling contexts up to 10 million tokens, and OpenAI's Sora, a text-to-video model, have sparked discussions about the future of AI, particularly the role and potential demise of Retrieval Augmented Generation (RAG). Gemini 1.5 Pro supports ultra-long contexts of up to 10 million tokens and multimodal data processing. In a "needle-in-a-haystack" evaluation method, Gemini 1.5 Pro achieves 100% recall from up to 530,000 tokens and maintains over 99.7% recall from up to 1M tokens. Even with a super long document of 10M tokens, the model retains an impressive 99.2% recall rate. While Gemini excels in managing extended contexts, it grapples with persistent challenges encapsulated as the 4Vs: Velocity, Value, Volume, and Variety. LLMs’ 4Vs Challenges include hurdles in achieving sub second response times for extensive contexts, considerable inference costs associated with generating high-quality answers in long contexts, vastness of unstructured data that may not be adequately captured by an LLM, and diverse range of structured data. Strategies for optimizing RAG effectiveness include enhancing long context understanding, utilizing hybrid search for improved search quality, and leveraging advanced technologies to enhance RAG’s performance. The RAG framework is still a linchpin for the sustained success of AI applications. Its provision of long-term memory for LLMs proves indispensable for developers seeking an optimal balance between query quality and cost-effectiveness.