AI Basics
Foundations
Machine Learning
Machine Learning (ML) is the process of training a model to perform a task. ML involves providing a dataset of examples of the task, and then training a model on that dataset. The model is then able to perform the task.
Large Language Models (LLMs)
Large Language Models (LLMs) are advanced natural language processing models designed to understand and generate human-like text. Trained on massive amounts of data, LLMs capture intricate language patterns and structures, allowing them to perform various tasks, such as text summarization, translation, question-answering and even holding a conversation. Examples of LLMs include GPT-4, Cohere, Bard, and PaLM and more. These models leverage the power of deep learning and the Transformer architecture to achieve state-of-the-art performance in many language-related tasks. By fine-tuning LLMs on specific domains or tasks, they can be adapted to a wide range of applications, making them a valuable resource in the field of artificial intelligence and natural language processing.
Prompt
A prompt is the primary way of interacting with AI systems. It is the input that the bot uses as the basis for it's response. Usually a prompt takes the form of a piece of text, but it can technically be any form of digitized data.
Model Improvements
Prompt Engineering
Prompt Engineering is the process of creating a prompt that will generate a desired response from a bot. Prompt engineering involves 2 step cycle:
- Compile a new prompt
- Analyze the response to the prompt
Fine Tuning
Fine tuning is the process of updating an existing bot with new data. Fine tuning a bot involves providing a dataset of text, and then fine tuning the bot on that dataset.
Embeddings
Embeddings are a set of vectors that represent a piece of text. Embeddings are used to represent a piece of text in a way that is easier for a model to understand.