LLM security focuses on safeguarding large language models against various threats that can compromise their functionality, integrity, and the data they process.
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Retrieval-Augmented Generation (RAG) merges LLMs with retrieval systems to boost output quality. Fine-tuning LLMs tailors them to specific needs on given datasets.
Fine-tuning Large Language Models (LLMs) involves adjusting pre-trained models on specific datasets to enhance performance for particular tasks.
Generative AI creates content across text, images, music, audio, and video using large, pre-trained models for tasks like summarization, Q&A, and classification.
A “prompt” is an input to a natural language processing (NLP) model. It contains user instructions that tell the model what kind of output is desired.
AI copilots are digital assistants using AI, often large language models (LLMs), to help with tasks like code generation, creative writing, and decision making.
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Prompt engineering involves crafting inputs (prompts) that guide a large language model (LLM) to generate desired outputs.
LLM application development involves creating software applications that leverage LLMs like OpenAI GPT or Meta LLaMA to generate, or manipulate natural language.