>"Using DSPy to automatically create, evaluate, and optimize your prompts"
Manual prompt engineering is often slow and unreliable due to unpredictable inputs. DSPy addresses this by treating prompt development like traditional ML training. It automatically generates, evaluates (using "LLM-as-a-judge"), and optimizes prompts based on high-level task descriptions, providing a faster, more systematic way to build robust LLM applications.
Manual prompt engineering is often slow and unreliable due to unpredictable inputs. DSPy addresses this by treating prompt development like traditional ML training. It automatically generates, evaluates (using "LLM-as-a-judge"), and optimizes prompts based on high-level task descriptions, providing a faster, more systematic way to build robust LLM applications.
This page details the topic namers available in Turftopic, allowing automated assignment of human-readable names to topics. It covers Large Language Models (local and OpenAI), N-gram patterns, and provides API references for the `TopicNamer`, `LLMTopicNamer`, `OpenAITopicNamer`, and `NgramTopicNamer` classes.
