Generative AI has the power to transform work, but many enterprises are struggling to build accurate, production-ready AI apps at a pace that meets the needs of their people. In fact, a survey<\/a> of 500 executives and AI professionals found that most in-house generative AI projects are mediocre or worse, as only 17% indicate they are excellent. B\u200cy abstracting away the complexities of generative AI, our full-stack platform of Writer-built LLMs, graph-based RAG<\/a>, AI guardrails, and development tools<\/a> makes it easy for organizations like Vanguard, Intuit, L\u2019Oreal, and Accenture to deliver impactful ROI. With Writer, enterprises can quickly build and deploy a wide range of AI apps, including text generation apps, chat apps, and more.<\/p>\n\n\n\n We\u2019re excited to share that we\u2019ve made a significant set of upgrades to how companies can build chat apps in Writer that make them even more powerful, transparent, and easy to use. These new capabilities include built-in RAG to analyze up to 10 million words, explainable AI features, dedicated modes, voice rewrites, custom instructions, and more. These enhancements are now available with chat apps built in AI Studio<\/a> as well as Ask Writer, our prebuilt chat app available to all users.<\/p>\n\n\n\n LLMs on their own, have a limited understanding of your products and business. To generate accurate and contextual insights and outputs, AI apps must have access to your most important files and documents. With chat apps, you can now upload files with up to 10 million words \u2014 the equivalent of 20,000 pages \u2014 and ask questions, conduct research, or generate outputs.<\/p>\n\n\n\n This new capability is made possible by our unique graph-based retrieval-augmented generation<\/a> (RAG) technology, which also powers Writer Knowledge Graph<\/a>. RAG is a natural-language technique used for question-answering. When given a question, it retrieves contextually relevant data points from a large corpus of uploaded data and passes it to LLMs to generate an accurate answer. In a recent RAG benchmarking report<\/a> that evaluated eight popular RAG approaches, Writer RAG achieved first place<\/strong> in accuracy as measured by the RobustQA benchmark. Leading enterprises, such as Commvault<\/a>, use our RAG technology to build powerful and accurate AI apps connected to their internal data and knowledge.<\/p>\n\n\n\n By building RAG directly into our chat apps, we\u2019re empowering enterprises to tackle powerful new use cases. Here are just a few examples of what Writer customers have already taken into production:<\/p>\n\n\n\nBuilt-in RAG to analyze up to 10 million words<\/h2>\n\n\n\n
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