Tag
large language models
Large language models are becoming a core layer of AI systems, shaping how teams train, evaluate, prompt, and deploy models. This topic covers model safety, explainability, inference cost, and the business deals that determine who gets access to compute and capability.
5 articles
AE-LLM aims to make LLMs more efficient
AE-LLM proposes adaptive efficiency optimization for large language models, but the provided source does not include benchmark details.
Google Plans $40B Bet on Anthropic
Alphabet may invest up to $40 billion in Anthropic, deepening a rival partnership as Google races to secure more AI capacity.
Mythos, Anthropic’s unreleased AI model, explained
Anthropic says Mythos is too dangerous to ship. Here’s what its 73% hacking score, 31-point math gain, and limited rollout mean.
LLMs plus knowledge graphs for ML explainability
A manufacturing XAI method uses a knowledge graph plus an LLM to turn ML results into clearer, more user-friendly explanations.
Prompt Engineering, Explained Without the Hype
Prompt engineering turns vague requests into usable AI outputs. AWS breaks down the methods, use cases, and tradeoffs behind better prompts.