Building LLM-Powered Enrollment Tools with BASIC

How General Bots bridges human knowledge with actionable tools

LLM Tools

BASIC Programming

Tech

🔄 LLM-Tool Integration Architecture

🧠 The Fundamental Problem

Why simple Q&A isn't enough for real-world applications

Standard LLM Knowledge

User: What's the capital of Italy?

AI: The capital of Italy is Rome.

Pure information recall - no action required

Real-World Scenario

User: I want to enroll in the Computer Science program

AI: I can't perform that action.

Requires system integration - this is where BASIC tools come in

🚀 The General Bots Solution

We extend LLMs with executable tools written in BASIC that can:

Collect structured data (enrollment forms)
Interact with databases and CSV files
Trigger real system actions
Maintain audit logs

🔁 Enrollment Process Flow

🔧 Anatomy of an Enrollment Tool

Project Directory Structure

How General Bots organizes tool implementations

BotServer/
└── templates/
    └── edu.gbai/
        ├── edu.gbdata/
        │   └── enrollments.csv
        ├── edu.gbdialog/
        │   ├── enrollment.bas
        │   └── start.bas
        └── edu.gbot/
            └── config.csv

gbdata/

Contains all data files (CSV, JSON, etc.)

gbdialog/

BASIC files defining tool behavior

gbot/

Configuration and metadata

📝 BASIC Syntax Guide

🎮 Conversation based on that code

Experience how the enrollment tool works in practice

I'd like to enroll in the Computer Science program

Certainly! Let's begin your enrollment. What's your full name?

[enrollment.bas triggered]

Maria Silva

Thanks, Maria. Could you provide your date of birth in DD/MM/YYYY format?

15/07/2002

Thank you. What email address should we use for communications?

Behind the Scenes

Tool Activated: enrollment.bas

Parameters Collected: name, birthday, email, etc.

Data Saved To: enrollments.csv

🛠️ Building Your Own Tools

1. Define Parameters

PARAM fieldname AS type LIKE "example" 
DESCRIPTION "parameter explanation"

Declare each piece of data you need to collect with type safety and examples.

2. Implement Logic

IF condition THEN action
SAVE "file.csv", data1, data2
RETURN "message"

Add any conditional logic, data saving, or API calls needed.

3. Configure Integration

# In config.csv
Answer Mode,tool
Start Dialog,start

Set up how and when your tool should be triggered.

🌐 Application Areas

⚖️ Comparing Approaches

Traditional LLMs vs. LLM + BASIC Tools

Understanding the key differences in capability and functionality

Feature	Traditional LLM	LLM + BASIC Tools
Data Storage	❌ No persistent storage	✅ CSV/database storage
Structured Input	⚠️ Inconsistent extraction	✅ Typed parameters with validation
System Actions	❌ Cannot perform actions	✅ Can execute real-world tasks
Context Window	⚠️ Limited by token count	✅ External data access
Audit Trail	❌ No built-in logging	✅ Built-in logging capabilities
Knowledge	✅ Broad knowledge base	✅ Combines broad knowledge with specialized tools

📊 Data Flow Architecture

🚀 Real-World Use Cases

Enrollment Management

Streamlining registration

•Student information collection and validation
•Course catalog browsing and selection
•Automatic waitlist management
•Prerequisite checking and enforcement

Academic Advising

AI-powered student guidance

•Degree progress tracking and reporting
•Personalized course recommendations
•Scheduling conflict detection
•Academic performance monitoring

🔍 Implementation Considerations

Key challenges and solutions when implementing LLM-BASIC tools

Technical Challenges

▲
Intent Recognition Accuracy
LLMs may incorrectly trigger tools based on ambiguous requests
▲
Data Security
Need for proper authentication and authorization controls

Best Practices

✓
Clear Parameter Definitions
Define precise data types and validation constraints
✓
Comprehensive Error Handling
Design tools with graceful failure modes and clear messages

💡 Integration Tips

When implementing LLM-BASIC integration for systems:

Start with simple, well-defined tools before complex workflows
Add comprehensive logging to track tool usage and errors
Implement user feedback mechanisms to improve recognition
Consider privacy requirements for data

Combine LLM knowledge with executable BASIC tools to create truly capable AI systems that can handle real-world tasks and beyond

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