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LLM security focuses on safeguarding large language models against various threats that can compromise their functionality, integrity, and the data they process. Read more

Retrieval-Augmented Generation (RAG) merges LLMs with retrieval systems to boost output quality. Fine-tuning LLMs tailors them to specific needs on given datasets. Read more

Fine-tuning Large Language Models (LLMs) involves adjusting pre-trained models on specific datasets to enhance performance for particular tasks. Read more

Generative AI creates content across text, images, music, audio, and video using large, pre-trained models for tasks like summarization, Q&A, and classification. Read more

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. Read more

Prompt engineering involves crafting inputs (prompts) that guide a large language model (LLM) to generate desired outputs. Read more

LLM application development involves creating software applications that leverage LLMs like OpenAI GPT or Meta LLaMA to generate, or manipulate natural language. Read more