Author: Publisher

  • Reducing LLM utilization, cost and latency with RAG

    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?

    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

    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

    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

    1. Retrieval Augment Generation 2. Holistic AI Solutions 3. Streamlined 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

    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…

  • Exploring an open source, local, semantic search powered file browser

    There is a rapidly growing demand in the market for open source, localised GenAI applications. Partially driven by: We are exploring an open source port of our proprietary toolchain which aims to run on an operating system layer, across all unstructured documents, to allow for immediate, secure, semantic search capabilities. This implementation remedies a lot…

  • Understanding Reciprocal Rank Fusion (RRF) for Semantic Hybrid Search

    Hybrid search is an innovative technique that blends the precision of full-text search with the nuanced understanding of vector search to provide well-rounded search results. By executing a single query that incorporates both search and vector query parameters in parallel, and scoring the combined results using the Reciprocal Rank Fusion (RRF) algorithm, hybrid search offers…

  • Improving the accuracy of Generative AI

    Using Generative AI to recall crucial information from vector embeddings is very unreliable, even with the advent of Hybrid semantic searching. Our focus has been to use a mixture of techniques in order to improve recall rates to a non-human detectable level, meaning, that when a human runs a query, and RAG occurs, the final…