// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Parameters
These are the internal variables of a machine learning model that are learned from the training data. For neural networks, these are typically the weights and biases.
TECHNICAL DEFINITION
The internal variables of a machine learning model, such as weights and biases in a neural network, whose values are learned from data during training to optimize the model's performance.
BACKGROUND
Prompt engineering is the process of structuring natural language inputs to produce specified outputs from a generative artificial intelligence (GenAI) model. Context engineering is the related area of software engineering that focuses on the management of non-prompt and prompt contexts supplied to the GenAI model, such as system instructions, metadata, API tools and tokens.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Model Weights
- Biases
- Learnable Variables
USAGE NOTE
The quality of a model's parameters directly impacts its predictive accuracy.
DEVELOPERS
Organizations developing technology related to Parameters.
A leading AI research and deployment company known for developing large language models such as GPT series, which involve billions or trillions of parameters. They are at the forefront of AI engineering, scaling models, and advancing prompt design techniques to leverage these parameter-rich models.
Google's AI division, responsible for developing foundational AI models like Gemini and LaMDA. Their work involves extensive research into neural network architectures, scaling models with vast numbers of parameters, and the engineering challenges associated with training and deploying such systems.
Meta's AI research division develops large language models such as the Llama series, which are often open-sourced with various parameter counts. They contribute to the understanding and engineering of large-scale AI models and their efficient deployment.
A company focused on developing large, reliable, and steerable AI systems, notably the Claude family of models. Their work heavily involves the engineering of large parameter models and ensuring their safety and performance through careful prompt engineering and model alignment.
Hugging Face provides a platform and tools for building, training, and deploying machine learning models, particularly large language models. Their Transformers library is central to working with models of varying parameter sizes, enabling AI engineers to experiment, fine-tune, and apply models effectively.
While primarily a hardware company, NVIDIA is critical for AI engineering by providing the GPU infrastructure and software platforms (like CUDA, TensorRT, and NeMo) essential for training and deploying large-scale AI models with billions of parameters, enabling the entire ecosystem.
Microsoft offers a comprehensive suite of cloud services and tools for AI development, training, and deployment on Azure. This includes managed services for large language models, MLOps platforms, and infrastructure that enables AI engineers to scale and manage models with many parameters.
Cohere specializes in enterprise-grade large language models and related tools for developers. They focus on providing accessible and powerful LLMs, which inherently involves managing and optimizing models defined by their parameters for various business applications and supporting prompt engineering strategies.