Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
NeMo Curator delivers 17x faster data processing with measurable accuracy gains. See the GPU scaling benchmarks and real-world performance improvements for LLM training.
Learn how quality filtering and fuzzy deduplication create a tradeoff in LLM data curation, and how NeMo Curator uses GPU acceleration to handle both at scale.
When LLMs crash during long conversations, the culprit is often the KV cache, not GPU vRAM. Learn the tiered memory management strategy that scales LLM inference.
ByteDance's Seed-OSS-36B-Instruct brings 512K context, Apache 2.0 licensing, and a unique thinking budget feature. A deep dive into the model that challenges proprietary LLMs.
OpenAI released GPT-OSS, open-weight models with 120B and 21B parameters under Apache 2.0 licensing. Learn about the architecture, capabilities, and what this means for AI development.
How to plan and deploy azure ai foundry service: azure AI Foundry Agent Service provides a managed framework for building, managing, and deploying AI agents on Azure. Compare it to Semantic Kernel, AutoGen, and Copilot Studio.
LLM reasoning enables AI agents to solve complex problems through chain-of-thought, ReAct, and self-reflection techniques. Learn how reasoning scales test-time compute for better results.
Reinforcement Learning from Human Feedback (RLHF) aligns LLMs with human values through three training stages. Learn how RLHF works, why it matters, and how it produces better AI.
Eight practical strategies for improving LLM prompt consistency — from prompt decomposition and few-shot examples to temperature tuning and output format specification.
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