«`html 7 LLM Generation Parameters—What They Do and How to Tune Them In the realm of language model fine-tuning, understanding the seven parameters that govern large language model (LLM) generation is crucial for achieving desired outputs. These parameters influence aspects such as response length, randomness, novelty, and termination. Below, we analyze each parameter, its function,… →

ServiceNow AI Research Releases DRBench, a Realistic Enterprise Deep-Research Benchmark Understanding the Target Audience The primary audience for the DRBench benchmark includes AI researchers, enterprise software developers, data scientists, and business analysts, particularly those focused on improving the capabilities of AI agents in enterprise contexts. Their pain points often revolve around the challenge of accurately… →

«`html Understanding the Target Audience for Meta’s ARE + Gaia2 The target audience for Meta’s Agents Research Environments (ARE) and Gaia2 includes AI researchers, business managers, and technology decision-makers. These individuals are typically involved in the development, evaluation, and deployment of AI agents in various business contexts. Pain Points Difficulty in evaluating AI agents under… →

«`html Microsoft AI Debuts MAI-Image-1: An In-House Text-to-Image Model that Enters LMArena’s Top-10 Microsoft AI has introduced MAI-Image-1, its first image generation model developed entirely in-house. As of October 13, 2025, this model has debuted in the Top-10 of the LMArena text-to-image leaderboard. MAI-Image-1 is currently being tested publicly to collect community feedback, with plans… →

«`html How to Evaluate Your RAG Pipeline with Synthetic Data Evaluating LLM applications, particularly those using RAG (Retrieval-Augmented Generation), is crucial but often neglected. Without proper evaluation, it’s almost impossible to confirm if your system’s retriever is effective, if the LLM’s answers are grounded in the sources (or hallucinating), and if the context size is… →