Explore AIstify's latest reporting, research, and expert analysis tagged with "large language models", collected in one continuously updated archive.
An attention mechanism helps an AI model focus on the most relevant parts of its input when interpreting information or generating an output.
Fine-tuning adapts a pretrained AI model to a specific task, domain, or behavior by continuing training on targeted examples.
Retrieval-augmented generation (RAG) combines search with generative AI so responses use relevant documents, current data, and source context.
Prompt engineering is the practice of designing instructions, context, examples, and output rules that help generative AI perform tasks reliably.
LLMOps is the discipline of developing, deploying, evaluating, monitoring, and maintaining applications built around large language models.
Prompt injection is an AI security attack that uses malicious instructions to override a model’s rules, expose data, or trigger unauthorized tool actions.
Few-shot learning allows an AI model to recognize a task or pattern from only a small number of examples.
Anthropic is the AI safety and research company behind Claude, building frontier AI models and enterprise tools focused on reliability, interpretability, and steerability.
Pretraining teaches an AI model broad patterns from large datasets before the model is prompted or adapted for specific downstream tasks.
A parameter is a learned numerical value, such as a neural network weight, that determines how an AI model transforms input into output.
Model Context Protocol (MCP) is an open standard that connects AI applications with external tools, data sources, and reusable context.
An embedding is a numerical representation that places related words, images, documents, or other items close together in a mathematical space.
A mixture of experts (MoE) is an AI architecture that routes each input to selected subnetworks, increasing capacity without activating every parameter.
Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning method that trains small adapter weights instead of updating an entire AI model.
Instruction tuning trains an AI model on instruction-and-response examples so it follows natural-language tasks more reliably across different use cases.