💻Technicals

Technical Architecture

System Overview

FitSense's architecture combines advanced language models with event-driven processing to create a seamless fitness coaching experience. This document outlines the technical implementation and data flows within the system.

System Flow Diagram

Sequence Diagram

Component Details

Initialization Layer

• FitSense Interface: User-facing application interface

• LLM Handshake: Establishes secure connection with AI engine

• Prompt Injection: Loads specialized fitness knowledge and capabilities

• User Data Injection: Provides personalized context for interactions

AI Core

• LLM Engine: Processes natural language and generates responses

• Knowledge Base: Contains comprehensive fitness expertise

• Event Triggers: Recognizes specific commands for system actions

Data Layer

• Event Handler: Processes triggered actions

• Database: Stores user data and interaction history

• Dashboard: Provides real-time visualization of user progress

System Processes

1. Conversation Initialization

When a user starts interacting with FitSense:

1. System establishes LLM handshake

2. Injects specialized fitness prompts

3. Loads user context and history

2. Event Processing

When trigger words are detected:

1. LLM identifies specific command

2. Event Handler processes action

3. Database updates occur

4. Dashboard refreshes in real-time

3. Knowledge Integration

During conversations:

1. LLM queries Knowledge Base

2. Combines expertise with user context

3. Generates personalized responses