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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.