#### LLM blueprint {: #llm-blueprint data-category=gen-ai }
The saved blueprint, available to be used for [deployment](#deploying-from-playground). LLM blueprints represent the full context for what is needed to generate a response from an LLM; the resulting output can be compared within the [playground](#playground). This information is captured in the [LLM blueprint settings](#llm-blueprint-settings).

#### LLM blueprint components {: #llm-blueprint-components data-category=gen-ai }
The entities that make up the [LLM blueprint settings](#llm-blueprint-settings), this refers to the vector database, embedding model user to generate the vector database, LLM settings, system prompt, etc. These components can either be offered natively within DataRobot or can be brought in from external sources.

#### LLM blueprint draft {: #llm-blueprint-draft data-category=gen-ai }
A draft of the [LLM blueprint](#llm-blueprint) that can be used for experimentation and evaluation and ultimately saved as a blueprint that can be deployed.

#### LLM blueprint settings {: #llm-blueprint-settings data-category=gen-ai }
The parameters sent to the LLM to generate a response (in conjunction with the user-entered prompt). They include a single LLM, LLM settings, optionally a system prompt, and optionally a vector database. If no vector database is assigned, then the LLM uses its learnings from training to generate a response. LLM blueprint settings are configurable so that you can experiment with different configurations.


#### LLM payload {: #llm-payload data-category=gen-ai }
The bundle of contents sent to the LLM endpoint to generate a response. This includes the user prompt, LLM settings, system prompt, and information retrieved from the vector database.

#### LLM responses {: #llm-response data-category=gen-ai }
Generated text from the LLM based on the payload sent to an LLM endpoint.

#### LLM settings {: #llm-settings data-category=gen-ai }
Parameters that define how an LLM intakes a user prompt and generates a response. They can be adjusted within the LLM blueprint to alter the response. These parameters are currently represented by the "Temperature", "Token selection probability cutoff (Top P)", and "Max completion tokens" settings.
