Author: Publisher

  • How to develop a business case using AI

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    In this tutorial video, watch how you can use Veso Nexus, our flagship AI tool for professionals, to create businesses cases in a few minutes. With a few simple prompts and clicks, you’re now able to start fleshing out complex documents that used to take hours, in minutes. Speeding up Business Case Creation with Nexus…

  • Tool review: Openrouter.ai

    Tool review: Openrouter.ai

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    Finding the right LLM for your specific needs can feel like searching for a needle in a haystack. OpenRouter.ai is a great tool helping choose the right LLM for a use case. Let’s cover why this unified interface for Language Learning Models (LLMs) is an important tool in anyone’s kit. A Buffet of Brains at…

  • Agent Foundations in AI Alignment: A Technical Overview

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    The concept of Agent Foundations addresses critical challenges in AI alignment research, focusing on developing robust mathematical formulations for key concepts in artificial intelligence.This approach aims to create AI systems that reliably adhere to human values and intentions, even under intense optimization pressures. Problem Statement Naive approaches to AI alignment, such as training systems to…

  • AI Model Quantization: Leaner, faster, cheaper inference

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    AI Model Quantization: Leaner, faster, cheaper inference. Transforming high-precision weights into compact, efficient formats can revolutionize speed, reduce costs, and maintain accuracy. – At its core, model quantization is a technique aimed at reducing the size of a deep learning model. This is achieved by adjusting the model’s weights from high-precision formats (like 32-bit floats)…

  • Reducing LLM utilization, cost and latency with RAG

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    With RAG ( retrieval augmented generation ), only the most relevant context is added to construct a prompt payload for a language model. This means that, in most cases, your inference will be cheaper, faster, and more accurate, especially when dealing with multiple files, data lakes, and sources.

  • What is Retrieval Augmented Generation?

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    RAG (Retrieval Augmented Generation) is a technique that enhances the contextual awareness of large language models by feeding them information relevant to your prompt. This method proves more effective for ensuring relevance than traditional fine-tuning techniques. Imagine you have hundreds of documents, many of which reference a specific technique, such as engineering an airplane wing.…

  • Efficiently Extracting Data from Documents for Use with RAG

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    Overview A new methodology leveraging advanced parsing strategies has been introduced to enhance the efficiency and accuracy of data extraction from various document formats. This method is supported by the latest advancements in parsing research, designed to optimize data for use in Retrieval-Augmented Generation (RAG) applications. PDF Parser Guidelines HTML Parser Guidelines Text Parser Guidelines…

  • Retrieval Augmented Generation: Accelerating Real-World Task Performance

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    RAG combines the generative prowess of models like GPT with the precision of information retrieval, providing outputs that understand the context better than fine-tuning alone. Overview At Veso AI, we are proud to present our in-depth exploration of Retrieval Augmented Generation (RAG) and its significant impact on real workplace environments, particularly when handling critical tasks.…

  • Key AI Developments to Watch in 2024

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    1. Retrieval Augmented Generation 2. Holistic AI Solutions 3. Quantized AI Models 4. AI Management Hubs 5. AI Governance Focus 6. AI Legislation Challenges 7. Robust AI Evaluation 8. Diverse AI Model Applications 9. Multimodal AI Growth 10. Orchestrated AI Interactions 11. Multi-agent AI Dynamics 12. Real-Time Model Analytics 13. LLMs in Physical Spaces 14.…

  • Generative AI Systems on Closed vs. Open Networks: A Comparative Analysis

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    Overview In the rapidly evolving field of artificial intelligence, the dichotomy between closed and open networks is a subject of considerable debate. This article presents a comparative analysis of generative AI systems within these contrasting environments, with particular emphasis on the merits of data sovereignty and security offered by closed networks. Our investigation encompasses the…