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Corpus/🎇 Sparks/Key Areas of Rasa Syntax.md

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----
-tags:
-  - llm
-  - agent
-  - dev
----
-
-# Key Areas of Rasa Syntax:
-
-According to Gemini, 4 juli 2025
-
-- **YAML Configuration:**
-    
-    Rasa uses YAML files (e.g., `config.yml`, `domain.yml`, `endpoints.yml`) to define the core components of your chatbot, including the NLU pipeline, domain, and endpoint configurations. 
-    
-- **Training Data:**
-    
-    Training data, typically in Markdown format, is used to train the NLU model. It includes intents, entities, and examples of user input. 
-    
-- **Stories:**
-    
-    Stories, also in Markdown, define the dialogue flows of your chatbot, mapping user inputs to bot responses and actions. 
-    
-- **Actions:**
-    
-    Custom actions, written in Python, define the logic that your chatbot executes in response to user input or specific events. 
-    
-- **Rules:**
-    
-    Rules are a simplified way to define dialogue flows, often used for simple scenarios or to handle specific edge cases. 
-    
-- **Forms:**
-    
-    Forms are used to collect multiple pieces of information from the user in a structured way. 
-    
-- **Responses:**
-    
-    Responses define the text, buttons, or other elements that your chatbot sends back to the user. 
-    
-- **Flows:**
-    
-    Flows are used in Rasa Pro to define the overall structure of your conversation, including steps, conditions, and actions. 
-    
-- **Conditions:**
-    
-    Conditions are used to control the flow of conversation based on slot values, user input, or other factors. 
-    
-- **Command Line Interface (CLI):**
-    
-    Rasa provides a CLI for various tasks, including training models, running servers, and inspecting assistants. 
-    
-- **Session Management:**
-    
-    Rasa handles session management using session configuration, expiration times, and slot carryover.