Update 3-components-and-architecture-of-dialogue-systems.md

Author: kprks

learn-pr/philanthropies/architecture-of-thought/includes/3-components-and-architecture-of-dialogue-systems.md

@@ -1,49 +1,47 @@
-## Components and Architecture of Dialogue Systems
-
 AI-powered dialogue systems use a structured architecture designed to understand user input, manage context, and generate meaningful responses. Each component plays a specific role in supporting natural and helpful interactions between humans and machines. This lesson explores the foundational layers of these systems and explains how they work together to drive intelligent conversations.
 
-## User Interface (UI)
+## User interface (UI)
 
 The user interface is the access point where users interact with a dialogue system. It can appear as a text-based chatbot or a voice-based virtual assistant.
 
-- **Text-Based Interfaces** – Appear in messaging apps, websites, and customer support chatbots.
-- **Voice-Based Interfaces** – Appear in virtual assistants like Alexa or Cortana, allowing spoken input and output.
+- **Text-based interfaces** – Appear in messaging apps, websites, and customer support chatbots.
+- **Voice-based interfaces** – Appear in virtual assistants like Alexa or Cortana, allowing spoken input and output.
 
-A well-designed UI improves usability, sets expectations for user engagement, and helps guide the conversation’s flow.
+A well-designed UI improves usability, sets expectations for user engagement, and helps guide the conversation's flow.
 
-## Natural Language Processing (NLP) Engine
+## Natural language processing (NLP) engine
 
-The NLP engine enables the system to understand and interpret human language. It breaks down user input and extracts meaning to inform the system’s response.
+The NLP engine enables the system to understand and interpret human language. It breaks down user input and extracts meaning to inform the system's response.
 
 - **Tokenization** – Splits text into manageable parts, such as words or phrases.
-- **Entity Recognition** – Identifies specific data like names, locations, or dates.
-- **Intent Classification** – Determines what the user wants to accomplish.
+- **Entity recognition** – Identifies specific data like names, locations, or dates.
+- **Intent classification** – Determines what the user wants to accomplish.
 
 NLP transforms natural language into structured information that the system can act on.
 
-## Dialogue Management
+## Dialogue management
 
-Dialogue management tracks the conversation’s state and decides how the system should respond. It ensures that the interaction stays coherent and contextually relevant across multiple exchanges.
+Dialogue management tracks the conversation's state and decides how the system should respond. It ensures that the interaction stays coherent and contextually relevant across multiple exchanges.
 
 - Maintains user context and tracks conversation history.
 - Handles branching paths and follow-up questions.
 - Coordinates timing, transitions, and fallback responses.
 
 Effective dialogue management helps conversations feel more fluid and intuitive.
 
-## Knowledge Base
+## Knowledge base
 
 The knowledge base stores facts, policies, FAQs, or other data that the dialogue system references to deliver accurate answers.
 
 - Can be static (pre-written content) or dynamic (connected to live databases).
-- Enhances the chatbot’s ability to handle domain-specific queries.
+- Enhances the chatbot's ability to handle domain-specific queries.
 - Supports deeper responses beyond general small talk.
 
 This knowledge component allows the system to be genuinely helpful in practical use cases like customer service or technical support.
 
-## Response Generation
+## Response generation
 
-The response generation layer uses AI models to craft replies that feel natural, informative, and aligned with the conversation’s context.
+The response generation layer uses AI models to craft replies that feel natural, informative, and aligned with the conversation's context.
 
 - GPT models generate responses dynamically based on intent and prior dialogue.
 - The tone, vocabulary, and structure match the audience or scenario.