N.L.D.S. User Guide & Tutorials

Welcome to N.L.D.S.

The Natural Language Detection System (N.L.D.S.) is the revolutionary Tier 0 component of JAEGIS v2.2 that transforms how you interact with AI systems. Instead of learning complex commands or syntax, simply describe what you want in natural language, and N.L.D.S. will understand, analyze, and translate your intent into optimized JAEGIS commands.

Table of Contents

1. Getting Started

2. Basic Usage

3. Advanced Features

4. Best Practices

5. Tutorials

6. Troubleshooting

7. FAQ

Getting Started

What is N.L.D.S.?

N.L.D.S. is an intelligent interface that:

• Understands your natural language input

• Analyzes your intent across multiple dimensions

• Translates your request into optimized JAEGIS commands

• Integrates with external AI services for enhanced processing

• Delivers results with high confidence and speed

Key Benefits

• 🧠 Natural Communication - Speak to AI in your own words

• ⚡ Lightning Fast - <500ms response time guaranteed

• 🎯 High Accuracy - ≥85% confidence threshold maintained

• 🔗 Seamless Integration - Works with existing JAEGIS workflows

• 🚀 Enhanced Intelligence - Powered by A.M.A.S.I.A.P. protocol

System Requirements

• API Access: Valid JAEGIS API key or JWT token

• Internet Connection: Required for external AI integrations

• Browser: Modern browser for web interface (Chrome, Firefox, Safari, Edge)

• Optional: Python 3.8+ or Node.js 16+ for SDK usage

Basic Usage

Web Interface

1. Access the Interface

   • Navigate to https://app.jaegis.ai or your local instance

   • Log in with your credentials

   • Select "N.L.D.S. Interface" from the main menu

2. Submit Your Request

   Input: "Analyze our website's performance and suggest improvements"

3. Review the Results

   • Enhanced input with context

   • Confidence score and analysis

   • Generated JAEGIS command

   • Execution options

API Usage

Simple Processing

curl -X POST https://api.jaegis.ai/v1/process \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-api-key" \
  -d '{
    "input_text": "Create a marketing plan for our new product launch"
  }'

Enhanced Processing

curl -X POST https://api.jaegis.ai/v1/process \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-api-key" \
  -d '{
    "input_text": "Optimize our database performance",
    "mode": "enhanced",
    "enable_amasiap": true,
    "context": {
      "domain": "technology",
      "urgency": "high",
      "audience": "technical_team"
    }
  }'

Python SDK

from nlds_sdk import NLDSClient

# Initialize client
client = NLDSClient(api_key="your-api-key")

# Process natural language input
result = await client.process(
    input_text="Develop a comprehensive training program for new employees",
    mode="comprehensive",
    context={
        "domain": "human_resources",
        "urgency": "medium",
        "audience": "management"
    }
)

# Access results
print(f"Enhanced Input: {result.enhanced_input}")
print(f"Confidence: {result.confidence_score:.2%}")
print(f"JAEGIS Command: {result.jaegis_command}")

Advanced Features

A.M.A.S.I.A.P. Protocol

The Automatic Multi-dimensional Analysis, Synthesis, Intelligence, and Adaptive Processing protocol enhances your input automatically:

# Enable A.M.A.S.I.A.P. for maximum enhancement
result = await client.process(
    input_text="Improve customer satisfaction",
    enable_amasiap=True,
    amasiap_options={
        "research_depth": "comprehensive",
        "context_enrichment": True,
        "temporal_analysis": True
    }
)

Multi-dimensional Analysis

N.L.D.S. analyzes your input across three dimensions:

1. Logical Analysis - Structure, requirements, reasoning

2. Emotional Analysis - Sentiment, tone, emotional context

3. Creative Analysis - Innovation potential, creative patterns

# Request specific analysis types
result = await client.analyze(
    input_text="Design an innovative user interface",
    analysis_types=["logical", "creative"],
    depth_level=4
)

print(f"Logical Confidence: {result.logical_analysis.confidence:.2%}")
print(f"Creative Score: {result.creative_analysis.creativity_score:.2%}")

Context Enhancement

Provide context to improve processing accuracy:

# Rich context for better results
context = {
    "domain": "e-commerce",
    "company_size": "startup",
    "budget": "limited",
    "timeline": "Q4 2025",
    "target_audience": "millennials",
    "current_challenges": ["user_retention", "conversion_rate"]
}

result = await client.process(
    input_text="Increase online sales",
    context=context
)

Best Practices

Writing Effective Inputs

✅ Good Examples

"Analyze our customer feedback data and identify the top 3 pain points affecting user satisfaction"

"Create a comprehensive project timeline for developing a mobile app with user authentication, payment processing, and real-time notifications"

"Develop a cost-effective marketing strategy to increase brand awareness among tech-savvy professionals aged 25-40"

❌ Avoid These

"help"
"do something"
"fix it"
"make it better"

Optimization Tips

1. Be Specific - Include details about what, why, and how

2. Provide Context - Add domain, urgency, and audience information

3. Set Clear Goals - Define what success looks like

4. Include Constraints - Mention budget, time, or resource limitations

5. Use Action Words - Start with verbs like "analyze," "create," "develop"

Context Guidelines

Context Field	Purpose	Examples
domain	Industry/field	"technology", "healthcare", "finance"
urgency	Priority level	"low", "medium", "high", "urgent"
audience	Target audience	"executives", "developers", "customers"
timeline	Time constraints	"Q4 2025", "next month", "ASAP"
budget	Financial limits	"unlimited", "limited", "$10,000"

Tutorials

Tutorial 1: Basic Content Creation

Objective: Create a blog post outline using N.L.D.S.

Step 1: Prepare your input

"Create a detailed blog post outline about sustainable technology trends for 2025, targeting business executives who want to understand environmental impact and ROI"

Step 2: Add context

context = {
    "domain": "sustainability",
    "audience": "business_executives",
    "content_type": "blog_post",
    "tone": "professional",
    "length": "2000_words"
}

Step 3: Process with N.L.D.S.

result = await client.process(
    input_text="Create a detailed blog post outline about sustainable technology trends for 2025",
    mode="enhanced",
    context=context
)

Step 4: Review and execute

• Check confidence score (should be >85%)

• Review generated JAEGIS command

• Submit for execution

Tutorial 2: Technical Analysis

Objective: Analyze system performance issues

Step 1: Describe the problem

"Our web application is experiencing slow response times during peak hours. Analyze the potential causes and recommend optimization strategies for a Node.js backend with PostgreSQL database serving 10,000+ concurrent users"

Step 2: Provide technical context

context = {
    "domain": "technology",
    "tech_stack": ["nodejs", "postgresql", "redis"],
    "scale": "10000_concurrent_users",
    "urgency": "high",
    "audience": "development_team"
}

Step 3: Request comprehensive analysis

result = await client.analyze(
    input_text=technical_description,
    analysis_types=["logical", "creative"],
    depth_level=5,
    context=context
)

Tutorial 3: Strategic Planning

Objective: Develop a business strategy

Step 1: Define strategic objective

"Develop a comprehensive market entry strategy for launching our AI-powered project management tool in the European market, considering regulatory requirements, competitive landscape, and localization needs"

Step 2: Strategic context

context = {
    "domain": "business_strategy",
    "market": "european_union",
    "product_type": "saas_tool",
    "target_segment": "enterprise",
    "timeline": "12_months",
    "budget": "500k_euros"
}

Step 3: Generate strategic plan

result = await client.process(
    input_text=strategic_objective,
    mode="comprehensive",
    enable_amasiap=True,
    context=context
)

Tutorial 4: Batch Processing

Objective: Process multiple related requests efficiently

Step 1: Prepare batch inputs

batch_inputs = [
    {
        "id": "analysis_1",
        "input_text": "Analyze Q3 sales performance",
        "context": {"domain": "sales", "period": "Q3_2025"}
    },
    {
        "id": "analysis_2", 
        "input_text": "Identify top performing products",
        "context": {"domain": "product", "metric": "revenue"}
    },
    {
        "id": "strategy_1",
        "input_text": "Recommend Q4 sales strategies",
        "context": {"domain": "strategy", "timeline": "Q4_2025"}
    }
]

Step 2: Submit batch request

batch_result = await client.batch_process(
    inputs=batch_inputs,
    options={
        "parallel_processing": True,
        "max_concurrent": 3
    }
)

Step 3: Process results

for result in batch_result.results:
    print(f"ID: {result.id}")
    print(f"Confidence: {result.confidence_score:.2%}")
    print(f"Command: {result.jaegis_command.command_type}")
    print("---")

Troubleshooting

Common Issues

Low Confidence Scores

Problem: Confidence score below 85% Solutions:

• Add more specific details to your input

• Provide relevant context information

• Use action-oriented language

• Break complex requests into smaller parts

Slow Response Times

Problem: Processing takes longer than expected Solutions:

• Check system status at /health endpoint

• Reduce analysis depth level

• Disable A.M.A.S.I.A.P. for faster processing

• Use caching for repeated requests

Authentication Errors

Problem: 401 Unauthorized responses Solutions:

• Verify API key is valid and active

• Check token expiration

• Ensure proper Authorization header format

• Refresh JWT token if expired

Rate Limiting

Problem: 429 Too Many Requests Solutions:

• Implement exponential backoff

• Check rate limit headers

• Upgrade to higher tier plan

• Use batch processing for multiple requests

Error Codes Reference

Error Code	Description	Solution
VALIDATION_ERROR	Input validation failed	Check input format and length
CONFIDENCE_TOO_LOW	Result below threshold	Improve input specificity
PROCESSING_TIMEOUT	Request timed out	Reduce complexity or retry
EXTERNAL_API_ERROR	External service issue	Check service status, retry later
QUOTA_EXCEEDED	Usage quota exceeded	Upgrade plan or wait for reset

FAQ

General Questions

Q: What makes N.L.D.S. different from other AI interfaces? A: N.L.D.S. provides multi-dimensional analysis, confidence validation, and seamless integration with the JAEGIS agent system, ensuring reliable and actionable results.

Q: How accurate is N.L.D.S.? A: N.L.D.S. maintains a ≥85% confidence threshold and provides transparency about confidence levels for each result.

Q: Can I use N.L.D.S. for any type of request? A: N.L.D.S. is designed for business, technical, and creative tasks. It works best with specific, actionable requests.

Technical Questions

Q: What is the A.M.A.S.I.A.P. protocol? A: A.M.A.S.I.A.P. (Automatic Multi-dimensional Analysis, Synthesis, Intelligence, and Adaptive Processing) is our proprietary protocol that enhances input with research, context, and intelligent processing.

Q: How fast is N.L.D.S.? A: N.L.D.S. processes requests in <500ms with a capacity of 1000 requests per minute.

Q: Can I integrate N.L.D.S. with my existing systems? A: Yes, N.L.D.S. provides RESTful APIs, WebSocket connections, and SDKs for popular programming languages.

Usage Questions

Q: How do I improve my results? A: Provide specific details, relevant context, and clear objectives. Use the best practices guide for optimal results.

Q: What if my confidence score is low? A: Low confidence indicates unclear or vague input. Add more details, context, or break complex requests into smaller parts.

Q: Can I process multiple requests at once? A: Yes, use the batch processing API to handle multiple requests efficiently.

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User Guide Version: 1.0 Last Updated: July 26, 2025 For Support: [e-mail] Documentation: https://docs.jaegis.ai