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AI Development Use Cases

This guide provides detailed examples of AI development scenarios you can implement using the Local AI Cyber Lab.

🤖 Chatbot Development

Secure Customer Service Bot

A practical example of building a secure customer service chatbot.

graph LR
    A[User Input] --> B[AI Guardian]
    B --> C[Ollama LLM]
    C --> D[Response]
    D --> E[Security Check]
    E --> F[User]
    B -- Logs --> G[Langfuse]
    E -- Logs --> G

Implementation Steps

  1. Setup Base Model 

    ollama pull mistral

  2. Create Security Rules 

    # AI Guardian configuration
{
  "rules": {
    "pii_detection": true,
    "prompt_injection": true,
    "output_sanitization": true
  }
}

  3. Configure Monitoring 

    # Langfuse configuration
monitoring:
  events:
    - prompt_processing
    - response_generation
    - security_checks

🗣️ Voice Assistant Pipeline

Multilingual Voice Assistant

Example of creating a voice assistant that supports multiple languages.

graph TD
    A[Audio Input] --> B[Whisper ASR]
    B --> C[Text Processing]
    C --> D[Ollama LLM]
    D --> E[Response Text]
    E --> F[Coqui TTS]
    F --> G[Audio Output]

Implementation Steps

  1. Setup Speech Recognition 

    # Whisper configuration
whisper:
  model: large-v2
  language: auto
  task: transcribe

  2. Configure TTS 

    # Coqui TTS setup
{
  "model": "multilingual_speaker",
  "languages": ["en", "es", "fr"],
  "speakers": ["speaker_1", "speaker_2"]
}

🎨 Image Generation System

Secure Image Generation Pipeline

Example of building a secure image generation system with content filtering.

graph TD
    A[User Request] --> B[Content Filter]
    B --> C[ComfyUI]
    C --> D[Image Generation]
    D --> E[Safety Check]
    E --> F[Delivery]

Configuration

# ComfyUI workflow
nodes:
  - name: content_check
    type: safety_filter
    params:
      threshold: 0.85

  - name: stable_diffusion
    type: image_generator
    params:
      model: stable-diffusion-xl
      steps: 30

  - name: post_process
    type: image_safety
    params:
      check_nsfw: true

📊 MLOps Pipeline

Model Training and Deployment

Example of setting up a complete MLOps pipeline.

graph LR
    A[Data Prep] --> B[Training]
    B --> C[Evaluation]
    C --> D[MLflow Tracking]
    D --> E[Deployment]
    E --> F[Monitoring]

MLflow Configuration

mlflow:
  tracking_uri: http://localhost:5000
  experiment_name: llm_fine_tuning
  tags:
    environment: development
    project: customer_service_bot

🔄 Workflow Automation

Automated Content Pipeline

Example of automating content generation and validation.

graph TD
    A[Content Request] --> B[n8n Workflow]
    B --> C[AI Generation]
    C --> D[Validation]
    D --> E[Publishing]

n8n Workflow

{
  "nodes": [
    {
      "type": "trigger",
      "name": "Content Request"
    },
    {
      "type": "ollama",
      "name": "Generate Content"
    },
    {
      "type": "ai-guardian",
      "name": "Validate Content"
    }
  ]
}

🧪 Experiment Tracking

A/B Testing Different Models

Example of comparing different models' performance.

graph TD
    A[Test Cases] --> B[Model A]
    A --> C[Model B]
    B --> D[Results]
    C --> D
    D --> E[Analysis]

MLflow Experiment

# Track experiments
with mlflow.start_run():
    mlflow.log_param("model_name", "mistral")
    mlflow.log_param("dataset", "customer_queries")
    mlflow.log_metrics({
        "accuracy": 0.95,
        "latency": 120,
        "memory_usage": 4.2
    })

🔒 Security Integration

Secure AI Development Pipeline

Example of implementing security at every stage.

graph TD
    A[Development] --> B[Security Scan]
    B --> C[Testing]
    C --> D[Security Audit]
    D --> E[Deployment]
    E --> F[Monitoring]

Security Configuration

# AI Guardian rules
security_rules:
  - name: prompt_injection
    enabled: true
    threshold: 0.8

  - name: data_leakage
    enabled: true
    patterns:
      - type: pii
      - type: api_keys

  - name: output_validation
    enabled: true
    checks:
      - toxicity
      - bias
      - hallucination

📈 Performance Optimization

Model Optimization Pipeline

Example of optimizing model performance.

graph LR
    A[Baseline] --> B[Profiling]
    B --> C[Optimization]
    C --> D[Validation]
    D --> E[Deployment]

Performance Tracking

# MLflow performance tracking
metrics = {
    "inference_time": 120,
    "memory_usage": 4.2,
    "throughput": 100
}

mlflow.log_metrics(metrics)

Next Steps