Key Concepts and Explanations
Explanations for basic concepts/terminologies
Large Language Models (LLMs)
LLMs are advanced AI models designed to understand, generate, and manipulate human language. They are trained on extensive datasets, enabling them to perform a wide range of text-based tasks with high accuracy.
Tokens
In the context of LLMs, tokens refer to the smallest units of text processed by the model. A token can be as small as a single character or as large as a word. As an approximation, 1000 tokens are roughly equivalent to 750 words. For instance, this definition of a token (not including this sentence) is around 43 tokens.
LLM Evaluation Frameworks
The Keywords AI API employs sophisticated evaluation frameworks to assess LLMs. These frameworks analyze model performance across various dimensions, such as accuracy, response time, and contextual relevance. For example, a model may be evaluated based on its ability to generate coherent text, translate languages accurately, or understand user intent in conversational AI applications. By continuously monitoring and evaluating LLMs, the API ensures that only the most effective models are utilized for user requests.
LLM Observability
LLM observability refers to the ability to monitor and gather insights from LLM like GPT-3. LLM observability refers to the ability to monitor and gather insights from large language models (LLMs) like GPT-3. As LLMs become more advanced and integrated into different applications, it becomes important to understand their behavior and ensure they are performing as intended. Keywords AI API provides observability features that allow developers to monitor LLMs, logging their input and output, token usage, model choice, and customer sentiment analysis the help users understand the performance of their LLM API.