# 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

{% @mermaid/diagram content="graph TB
subgraph Initialization
UI\[FitSense Interface]
H\[LLM Handshake]
P\[Prompt Injection]
D\[User Data Injection]
end

```
subgraph AI Core
    LLM[LLM Engine]
    KB[Knowledge Base]
    ET[Event Triggers]
end

subgraph Data Layer
    EH[Event Handler]
    DB[(Database)]
    DASH[Dashboard]
end

UI --> H
H --> P
P --> D
D --> LLM
KB <--> LLM
LLM --> ET
ET --> EH
EH --> DB
DB --> DASH
DASH --> UI

classDef init fill:#e6f3ff,stroke:#333,stroke-width:2px;
classDef core fill:#f9f9f9,stroke:#333,stroke-width:2px;
classDef data fill:#f3f3f3,stroke:#333,stroke-width:2px;

class UI,H,P,D init;
class LLM,KB,ET core;
class EH,DB,DASH data;" %}
```

### Sequence Diagram

{% @mermaid/diagram content="sequenceDiagram
participant User
participant UI as FitSense UI
participant LLM as LLM Engine
participant KB as Knowledge Base
participant EH as Event Handler
participant DB as Database
participant Dash as Dashboard

```
Note over UI,KB: Initialization Phase
UI->>LLM: 1. LLM Handshake
UI->>LLM: 2. Prompt Injection
UI->>LLM: 3. User Data/Context Injection

Note over LLM,KB: Knowledge Integration
LLM->>KB: Query Fitness Knowledge
KB-->>LLM: Return Domain Expertise

Note over User,Dash: Interaction Flow
User->>UI: Send Message/Command
UI->>LLM: Forward to LLM

alt Event Trigger Detected (e.g., "log lift")
    LLM->>EH: Trigger Event
    EH->>DB: Update User Data
    DB->>Dash: Real-time Update
    Dash-->>UI: Display Changes
    UI-->>User: Show Confirmation
else Normal Conversation
    LLM->>KB: Query Knowledge Base
    KB-->>LLM: Return Relevant Info
    LLM-->>UI: Generate Response
    UI-->>User: Display Response
end" %}
```

### 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