RAG

The CoRAG Deal: RAG Without the Privacy Plot Twist The tension is growing: organizations want to co-train AI systems to improve performance, but data privacy concerns make collaboration difficult. Medical institutions, financial firms, and government agencies all sit on valuable question-answer (QA) data — but they can’t just upload it to a shared cloud to train a better model. This is the real challenge holding back Retrieval-Augmented Generation (RAG) from becoming a truly collaborative AI strategy. Not the rise of large context windows. Not LLMs like Gemini 2.5. But the walls between data owners. ...

Gemini 2.5 and the Rise of the 2 Million Token Era In March 2025, Google introduced Gemini 2.5 Pro with a 2 million token context window, marking a major milestone in the capabilities of language models. While this remains an experimental and high-cost frontier, it opens the door to new possibilities. To put this in perspective (approximate values, depending on tokenizer): 📖 The entire King James Bible: ~785,000 tokens 🎭 All of Shakespeare’s plays: ~900,000 tokens 📚 A full college textbook: ~500,000–800,000 tokens This means Gemini 2.5 could, in theory, process multiple entire books or large document repositories in one go—though with substantial compute and memory costs that make practical deployment currently limited. ...

Introduction Recently, 01.AI launched its enterprise AI platform, aiming to provide businesses with access to open-source LLMs, retrieval-augmented generation (RAG), model fine-tuning, and AI-powered assistants. This move is part of 01.AI’s broader effort to demonstrate relevance in the ongoing AI arms race, especially as the company has previously secured significant funding under the reputation of Li Kaifu. Given the rapid evolution of AI, 01.AI faces mounting pressure to show tangible business value to its investors—yet, its latest offering falls into the common trap of many AI enterprise solutions: prioritizing model deployment over true business integration. ...