Large Language Models (LLMs) are powerful models trained on large datasets to comprehend and generate content. They are based on transformer models, which have positional encodings and self-attention as key features. LLMs have a wide range of applications, from AI assistants and chatbots to translation, code generation, summarization, and search engines. There are various LLMs available, each with different architectures, training objectives, and computational resources. The way LLMs are prompted and the parameters set for their responses also play a crucial role in obtaining desired outputs. Prompt engineering techniques and approaches like zero-shot, few-shot, chain of thought prompting, and in-context learning can further enhance LLM performance. Additionally, tools like vector databases and Retrieval Augmented Generation (RAG) can improve the accuracy and recency of LLM responses. However, the choice of LLM and prompt engineering techniques depend on specific use cases and objectives.
https://flyte.org/blog/getting-started-with-large-language-models-key-things-to-know#what-are-llms