// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Sampling
The process of selecting the next word or token from a probability distribution generated by an AI model, influencing the creativity and randomness of its output.
TECHNICAL DEFINITION
Sampling in generative AI refers to the stochastic process of selecting the next token from the probability distribution predicted by a language model, often controlled by parameters like temperature, top-k, or top-p (nucleus sampling), to balance creativity and coherence in the generated sequence.
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
- Token Selection
- Generation Strategy
- Decoding Strategy
- Stochastic Generation
USAGE NOTE
Different sampling techniques are used to control the determinism versus creativity of an LLM's output.
DEVELOPERS
Organizations developing technology related to Sampling.
As the creator of the GPT series of models, OpenAI develops and implements core sampling techniques like temperature and nucleus (top-p) sampling, which are exposed through its API to allow developers to control the randomness and creativity of model outputs.
A key player in the open-source AI ecosystem, Hugging Face develops and maintains the 'transformers' library, which provides a standardized and highly configurable 'generate' method that implements a wide variety of sampling and decoding strategies for countless models.
Through its DeepMind and Google AI labs, Google researches advanced decoding strategies for its large models like Gemini. This includes developing and refining sampling methods to improve output quality, coherence, and diversity in text generation tasks.
Developer of influential open-source models like the Llama series. Meta AI's work includes providing reference implementations for efficient inference, which incorporates various sampling algorithms that the open-source community adopts and builds upon.
NVIDIA develops software libraries like TensorRT-LLM that optimize and accelerate LLM inference on its GPUs. These libraries include highly-optimized kernels for sampling algorithms, enabling faster and more efficient text generation at scale.
Focused on AI safety, Anthropic develops sampling techniques for its Claude models designed to produce more reliable, predictable, and controllable outputs. Their research often involves adjusting the sampling process to mitigate harmful or nonsensical generation.
Known for creating powerful open-source models, Mistral AI engineers efficient inference solutions that include sophisticated and optimized sampling methods. Their work focuses on achieving high-quality text generation while maintaining computational efficiency.
As a provider of enterprise-focused language models, Cohere's platform gives developers fine-grained control over the generation process, including advanced sampling parameters, to help tailor model outputs for specific business applications and use cases.