Language Models (LLM)

Intro to Vision RAG: Smarter Retrieval for Visual Content in PDFs

Published

Apr 30, 2025

Author

Dr. Phil Winder

CEO

As visual data becomes increasingly central to enterprise content, traditional retrieval-augmented generation (RAG) systems often fall short when faced with richly visual documents like PDFs filled with charts, diagrams, and infographics. Vision RAG is a cutting-edge pipeline that leverages vision models to generate image embeddings, enabling intelligent indexing and retrieval of visual content.

In this session, you’ll explore the state of the art in visual RAG, see a live demo using open-source tools like VLLM and custom Python components, and learn how to integrate this capability into your own GenAI stack. The presentation will also highlight Helix, our secure GenAI platform, showcasing how Vision RAG fits into a scalable, enterprise-ready solution.

User Feedback in LLM-Powered Applications

Published

Mar 26, 2025

Author

Dr. Phil Winder

CEO

Building LLM-powered applications is challenging. But the most important challenge is when products are not useful to their users. This presentation is about the different ways you can gather feedback to improve LLM applications. I’ll review the state of the art, offer some practical tips, and share some examples.

Scaling GenAI to Production: Strategies for Enterprise-Grade AI Deployment

Published

Feb 17, 2025

Author

Natalia Kuzminykh

Associate Data Science Content Editor

The article examines the challenges of moving GenAI from prototypes to production. It highlights issues such as resource constraints, performance monitoring, cost management, and security, and suggests strategies for efficient scaling, robust guardrails, and continuous monitoring to ensure sustainable enterprise-grade deployments.

Enterprise AI Assistants: Combatting Fragmentation

Published

Jan 16, 2025

Author

Dr. Phil Winder

CEO

Enterprise AI Assistants unify disparate data sources, providing real-time insights and access control. Building domain-specific assistants and orchestrating them (hierarchical or federated) offers scalability, specialized features, and better performance than single-vendor solutions. Ultimately, organizations need a tailored approach that consolidates knowledge, fosters collaboration, and addresses evolving AI integration challenges.

Large Language Model Fine-Tuning via Context Stacking

Published

Dec 11, 2024

Author

Fine-tuning Large Language Models (LLMs) can be a resource-intensive and time-consuming process. Businesses often need large datasets and significant computational power to adapt models to their unique requirements. Attentio, co-founded by Julian and Lukas, is changing this landscape with an innovative technique called context stacking. In this video, we explore how this method works, why it is so efficient, and what it means for enterprises looking to embed custom knowledge directly into their AI models.

AI in 2024: A Year in Review

Published

Dec 9, 2024

Author

Dr. Phil Winder

CEO

In this reflective podcast, the team at Winder.AI — Dr. Phil Winder, Charles Humble, and Jonathan Hunter — take a deep dive into their year, discussing trends, lessons learned, and their vision for 2025.

Best LLMOps Tools: Comparison of Open-Source LLM Production Frameworks

Published

Oct 21, 2024

Author

Natalia Kuzminykh

Associate Data Science Content Editor

Discover how to deploy open-source LLMs using LLM agent frameworks, orchestration frameworks, and LLMOps platforms. Learn about serving frameworks like vLLM and Ollama, and explore LLMOps tools that enhance language model performance in production environments.

A Comparison of Open Source LLM Frameworks for Pipelining

Published

Aug 1, 2024

Author

Natalia Kuzminykh

Associate Data Science Content Editor

Discover top open source LLM frameworks and orchestration tools. Explore popular LLM projects, including LangChain and LlamaIndex, for seamless integration. Learn about Python LLM libraries, LLM agent frameworks, and the best tools for LLM development and orchestration.

Testing and Evaluating Large Language Models in AI Applications

Published

Jul 17, 2024

Author

Dr. Phil Winder

CEO

With the rapidly expanding use of large language models (LLMs) in downstream products, the need to ensure performance and reliability is crucial. But with random outputs and non-deterministic behaviour how do you know if you application performs, or works at all? This webinar offers a comprehensive, vendor-agnostic exploration of techniques and best practices for testing and evaluating LLMs, ensuring they meet the desired success criteria and perform effectively across varied scenarios.

Retrieval-Augmented Generation (RAG) Examples and Use Cases

Published

Jun 26, 2024

Author

Dr. Phil Winder

CEO

Watch our webinar to explore Retrieval Augmented Generation (RAG) and its integration with Large Language Models (LLMs). Learn about RAG use cases, advanced LLM architectures, and techniques to enhance AI applications. Ideal for professionals utilizing or interested in RAG and LLM-powered systems.