The [question generation](https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/) method combines structured data from the knowledge graph with unstructured data from the input documents to generate candidate questions related to specific entities.
Given a list of prior user questions, the question generation method uses the same context-building approach employed in [local search](local_search.md) to extract and prioritize relevant structured and unstructured data, including entities, relationships, covariates, community reports and raw text chunks. These data records are then fitted into a single LLM prompt to generate candidate follow-up questions that represent the most important or urgent information content or themes in the data.
Below are the key parameters of the [Question Generation class](https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/local_gen.py):
*`llm`: OpenAI model object to be used for response generation
*`context_builder`: [context builder](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object to be used for preparing context data from collections of knowledge model objects, using the same context builder class as in local search
*`system_prompt`: prompt template used to generate candidate questions. Default template can be found at [system_prompt](https://github.com/microsoft/graphrag/blob/main//graphrag/query/question_gen/system_prompt.py)
*`llm_params`: a dictionary of additional parameters (e.g., temperature, max_tokens) to be passed to the LLM call
*`context_builder_params`: a dictionary of additional parameters to be passed to the [`context_builder`](https://github.com/microsoft/graphrag/blob/main//graphrag/query/structured_search/local_search/mixed_context.py) object when building context for the question generation prompt
*`callbacks`: optional callback functions, can be used to provide custom event handlers for LLM's completion streaming events
