Inside Legion
2 minute read
From Yurts to Legion: The Next Chapter in Mission-Critical AI
Legion (formerly Yurts) delivers secure, orchestrated AI built for mission-critical workflows. Explore how we're empowering teams with intelligent, human-centered solutions for demanding environments.
3 minute read
Taking RAG Beyond Documents: How Legion Empowers Bidirectional Data Flow
Legion improves RAG by linking it with enterprise systems for seamless data flow. It supports custom integrations, AI-driven operations, and user adaptability, ideal for industries like aerospace and manufacturing.
6 minute read
The False Dichotomy: Navigating the Build vs. Buy Dilemma in Generative AI
Explore the build vs. buy dilemma in generative AI. Learn why adopting a hybrid strategy balances unique value and rapid deployment. Discover how businesses can achieve success by integrating Legion into their operations for both flexibility and immediate capabilities.
4 minute read
The Journey to Secure Generative AI: Balancing Fort Knox-Level Security with Flexibility
Discover how to secure generative AI without sacrificing flexibility. Learn about Legion's modular approach, which combines privacy-first design, adaptable infrastructure, and enterprise-grade security.
2 minute read
Enhancing AI Flexibility: Legion Integrates NVIDIA NIM Microservices for Secure, Scalable Solution
Legion integrates NVIDIA NIM microservices into its GenAI platform, expanding enterprise-ready LLMs, ensuring secure and flexible deployments. This enhances GPU-accelerated model variety, aiding secure, scalable AI solutions for enterprises.
9 minute read
Legion RAG: Performance That Doesn’t Break the Bank
Discover how Legion's RAG system matches Anthropic’s Contextual Retrieval performance on the Codebases dataset at just 1/300th of the cost. Explore our evaluation to see why Legion RAG is the smart, cost-effective choice for high-performance AI solutions.
5 minute read
Long Context Windows: Lots of Bucks, Where's the Bang?
GenAI's potential is vast, but practicality lags. Legion compares Retrieval Augmented Generation (RAG) to long context models for knowledge retrieval, favoring RAG's accuracy and scalability for enterprise use.
12 minute read
RAG Systems vs. LCW: Performance and Cost Trade-offs
Comparing RAG systems and LCW models on Needle in a Haystack benchmarks, showing RAG's superior performance and scalability, highlighting the need for better benchmarks for LCW models.
4 minute read
Chat Metrics for Enterprise-Scale Retrieval-Augmented Generation
Ensure your RAG system's efficiency with comprehensive chat metrics. Legion offers tools to monitor system performance live, using real-world data without labeled datasets, optimizing your retrieval and generation processes.
10 minute read
Enterprise Efficiency: The Performance vs. Cost Tradeoff in LLMs
Next-gen AI without breaking the bank! AWQ, a quantization method, boosts deploying LLMs' cost-effectiveness by cutting GPU needs, enabling wider access to advanced AI technology at lower costs.
2 minute read
Enterprise AI with Retrieval-Augmented Generation (RAG): Beyond LLMs
Learn how Retrieval Augmented Generation (RAG) is transforming enterprise AI and overcoming limitations of LLMs.
5 minute read
The Promise and Peril of Generative AI for Enterprises
Enterprises need more than a powerful AI model; they require a secure, efficient, and compliant platform, not just disparate tools, to deploy generative AI at scale.
3 minute read
Unlocking the Power of Generative AI: Why Enterprises Need a Comprehensive Platform
Discover why Legion is essential for businesses adopting generative AI, offering unmatched security, smooth integrations, and powerful analytics.
7 minute read
Navigating the Challenges of Fine Tuning and Catastrophic Forgetting
Learn to fine-tune LLMs with FIP & LoRA methods to beat "Catastrophic Forgetting" for robust AI applications across industries.
11 Minute Read
Illusions Unraveled: The Magic and Madness of Hallucinations in LLMs
TL;DR: We benchmarked various open-source LLMs, including Llama-v2-7b-chat, finding they hallucinate around 55% of the time in context-aware Q&A tasks without tuning.



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