[ { "title": "Why Gemini 1.5 (and other large context models) are bullish for RAG", "url": "https://medium.com/enterprise-rag/why-gemini-1-5-and-other-large-context-models-are-bullish-for-rag-ce3218930bb4", "relevance": 90 }, { "title": "RAG vs. Context-Window in GPT-4: accuracy, cost, & latency", "url": "https://ai88.substack.com/p/rag-vs-context-window-in-gpt4-accuracy-cost", "relevance": 85 }, { "title": "Understanding Tokens & Context Windows in Large Language Models", "url": "https://www.linkedin.com/pulse/understanding-tokens-context-windows-large-language-models-lepain-80p0e", "relevance": 80 }, { "title": "The Death of RAG? What a 10M Token Breakthrough Means for Developers", "url": "https://dev.to/dawiddahl/the-death-of-rag-what-a-10m-token-breakthrough-means-for-developers-3p24", "relevance": 75 }, { "title": "LongRoPE: Extending Context Windows of Pre-trained Language Models to 2048k Tokens", "url": "https://arxiv.org/abs/2402.13753", "relevance": 70 }, { "title": "The Future of RAG in an Era of Expanding Contexts in LLMs", "url": "https://blog.gopenai.com/the-future-of-rag-in-an-era-of-expanding-contexts-in-llms-162123f472e3", "relevance": 80 }, { "title": "De-Coded: Understanding Context Windows for Transformer Models", "url": "https://towardsdatascience.com/de-coded-understanding-context-windows-for-transformer-models-cd1baca6427e", "relevance": 75 }, { "title": "Context Window Size and Language Model Performance: A Balancing Act", "url": "https://ai.plainenglish.io/context-window-size-and-language-model-performance-balancing-act-2ae2964e3ec1", "relevance": 80 }, { "title": "Long context window models vs. RAG", "url": "https://medium.com/@jm_51428/long-context-window-models-vs-rag-a73c35a763f2", "relevance": 85 }, { "title": "Retrieval Augmented Generation for Large Language Models (LLM)", "url": "https://www.linkedin.com/pulse/retrieval-augmented-generation-large-language-models-llm-hoang-80p0e", "relevance": 70 } ]