Documents such as PDFs, graphs, charts, and dashboards are rich sources of data that, when extracted and organized, provide informative decision-making insights. From automating financial statement processing to improving business intelligence workflows, intelligent document processing is becoming a core component of AI solutions in enterprises. Organizations can accelerate the AI…
]]>Agents have been the primary drivers of applying large language models (LLMs) to solve complex problems. Since AutoGPT in 2023, various techniques have been developed to build reliable agents across industries. The discourse around agentic reasoning and AI reasoning models further adds a layer of nuance when designing these applications. The rapid pace of this development also makes it hard for…
]]>Applications requiring high-performance information retrieval span a wide range of domains, including search engines, knowledge management systems, AI agents, and AI assistants. These systems demand retrieval processes that are accurate and computationally efficient to deliver precise insights, enhance user experiences, and maintain scalability. Retrieval-augmented generation (RAG) is used to…
]]>Building a multimodal retrieval-augmented generation (RAG) system is challenging. The difficulty comes from capturing and indexing information from across multiple modalities, including text, images, tables, audio, video, and more. In our previous post, An Easy Introduction to Multimodal Retrieval-Augmented Generation, we discussed how to tackle text and images. This post extends this conversation…
]]>One challenge organizations face when customizing large language models (LLMs) is the need to run multiple experiments, which produces only one useful model. While the cost of experimentation is typically low, and the results well worth the effort, this experimentation process does involve “wasted” resources, such as compute assets spent without their product being utilized…
]]>Trillions of PDF files are generated every year, each file likely consisting of multiple pages filled with various content types, including text, images, charts, and tables. This goldmine of data can only be used as quickly as humans can read and understand it. But with generative AI and retrieval-augmented generation (RAG), this untapped data can be used to uncover business insights that…
]]>A retrieval-augmented generation (RAG) application has exponentially higher utility if it can work with a wide variety of data types—tables, graphs, charts, and diagrams—and not just text. This requires a framework that can understand and generate responses by coherently interpreting textual, visual, and other forms of information. In this post, we discuss the challenges of tackling multiple…
]]>Large language models (LLMs) have generated excitement worldwide due to their ability to understand and process human language at a scale that is unprecedented. It has transformed the way that we interact with technology. Having been trained on a vast corpus of text, LLMs can manipulate and generate text for a wide variety of applications without much instruction or training. However…
]]>ChatGPT has made quite an impression. Users are excited to use the AI chatbot to ask questions, write poems, imbue a persona for interaction, act as a personal assistant, and more. Large language models (LLMs) power ChatGPT, and these models are the topic of this post. Before considering LLMs more carefully, we would first like to establish what a language model does. A language model gives…
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