A conversational system prompt is used to calculate a response the agent should have, given the current state of the conversation. Its the equivalent to applying a system prompt to the current conversation state, but with all tools disabled so we only get the agents response.

Options

`model_name`	The name of the model that will be used to process the prompt.
`temperature`	Temperature to use with the model. The exact mathematical definition of temperature can vary depending on the model provider.
`max_tokens`	The maximum number of tokens to be included in the completion from the LLM provider.
`cache_variants`	The number of different variations to keep in the cache for this prompt. When the input data to the LLM is exactly the same, the prompt can be ‘cached’. By default only `1` variant is kept. But if your prompt is meant to do something like creative story writing or brainstorming, then you may want to increase the number of variants to say `100` or `10,000`, effectively eliminating the cache system.
`max_history_events`	The maximum number of conversation events to include in the conversation history data fed to the bot.

Output

`text`	The text containing the LLM’s response to be said back to the user
`prompt`	The raw text of the prompt that was sent up to the LLM provider.

Properties

type	LLM
needs conversation	true
uses content template	true
uses options template	true
customizable output schema	false

Prospera Platform Docs

Conversational System Prompt

Options

Output

Properties