Open AI Terminology:
API (Application Programming Interface):
A set of rules and protocols that allows different software applications to communicate with each other. OpenAI's API allows developers to integrate OpenAI's AI models into their own applications.
Context window
The context window is the maximum number of tokens (words or parts of words) that an AI model can process and consider simultaneously when generating a response. It is essentially the “memory” capacity of the model during an interaction or task. Models with larger context windows can handle larger attachments/prompts/inputs and sustain “memory” of a conversation for longer (Fogarty, 2023).
Embeddings:
Numerical representations of text that capture semantic meaning, allowing for tasks like similarity search and clustering.
Fine-tuning:
The process of further training a pre-trained model on a specific dataset to improve its performance on a particular task or domain.
Large Language Model (LLM)
Neural networks known as large language models work by forecasting word sequences. Large language models’ capabilities are rapidly advancing and continue to evolve with increased use. They can now hold dialogues, write prose, and scrutinize enormous text quantities from the internet.
Model:
A pre-trained AI system designed for a specific task, such as text generation (GPT models), image generation (DALL-E), or audio transcription (Whisper).
Prompt:
The input text or query provided to an AI model to elicit a desired response.
SDK:
A software development kit (SDK) is a collection of software development tools in one installable package. They facilitate the creation of applications by having a compiler, debugger and sometimes a software framework.
Token:
The fundamental unit of text processed by OpenAI models. A token can be a word, a part of a word, or even punctuation.
Temperature:
A parameter in text generation that controls the randomness or creativity of the output. Higher temperatures result in more varied and surprising responses.
Top-P (Nucleus Sampling):
A parameter that controls the diversity of generated text by selecting from the most probable tokens whose cumulative probability exceeds a certain threshold.
Transformer Model
Transformer models can process entire sentences simultaneously rather than in sequence, aiding in grasping context and the language’s long-term associations. This means these models can detect and interpret relationships between words and phrases in a sentence, even if they are positioned far apart from each other.
Commonly Used OpenAI APIs:
Audio API:
Includes functionalities for speech-to-text transcription (Whisper) and text-to-speech synthesis.
Completions API:
Used for generating text based on a given prompt, often used with GPT models.
Chat Completions API:
Designed for conversational AI applications, allowing for multi-turn interactions with models like GPT-3.5 and GPT-4.
DALL-E:
An AI model developed by OpenAI that generates images from textual descriptions.
Embeddings API:.
Used to create numerical embeddings of text, enabling semantic search, recommendation systems, and more.
Fine-tuning API:
Allows users to fine-tune OpenAI models on custom datasets to improve performance for specific application
GPT (Generative Pre-trained Transformer):
A family of large language models developed by OpenAI, known for their ability to generate human-like text.
Images API:
Facilitates the generation of images from text descriptions using models like DALL-E.
JSON-JavaScript Object Notation
JSON is a lightweight format for storing and transporting data
Moderation API:
Helps identify and filter out harmful or unsafe content in text.
Text to Speech
The Audio API provides a speech endpoint based on our GPT-4o mini TTS (text-to-speech) model. It comes with 11 built-in voices and can be used to:
Whisper:
An AI model developed by OpenAI for robust speech-to-text transcription.
Open AI API Reference
https://platform.openai.com/docs/api-reference/responses/create