Deep dives into agentic AI, LLM evaluation, synthetic data generation, model selection, and production AI engineering best practices.
8 of 314 articles
Traditional data curation pipelines for LLM training face critical bottlenecks in synthetic data generation, quality filtering, and semantic deduplication across text, image, and video modalities.
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.
NeMo Curator delivers 17x faster data processing with measurable accuracy gains. See the GPU scaling benchmarks and real-world performance improvements for LLM training.
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.
A comprehensive overview of AI agents — what they are, how they work, and the major platforms including GPT Agents, Gemini, Claude, Copilot, AutoGen, and AutoGPT.
NVIDIA's prompt-task-and-complexity-classifier categorizes prompts across 11 task types and 6 complexity dimensions using DeBERTa. Learn how it works and when to use it.
RAG strengthens LLM responses by grounding them in external knowledge sources. Learn how retrieval-augmented generation reduces hallucinations and enables real-time knowledge access.
