Dialog Basics

BASIC dialogs in General Bots are designed for the LLM era - you write tools and context setters, not complex conversation flows. The LLM handles the natural language understanding and conversation management.

Core Concepts

LLM Tools – BASIC scripts that become callable tools for the LLM
Context Management – SET_CONTEXT to provide knowledge to the LLM
Suggestions – Guide conversations with ADD_SUGGESTION
Memory – GET_BOT_MEMORY/SET_BOT_MEMORY for persistent data
Simple Syntax – English-like commands that anyone can write

Modern LLM-First Example

Inspired by real production bots, here's how modern BASIC works:

' Load context from memory
resume = GET_BOT_MEMORY("announcements")
context = GET_BOT_MEMORY("company_info")

' Give LLM the context it needs
SET_CONTEXT "announcements" AS resume
SET_CONTEXT "company" AS context

' Guide the conversation
CLEAR_SUGGESTIONS
ADD_SUGGESTION "announcements" AS "Show me this week's updates"
ADD_SUGGESTION "company" AS "Tell me about the company"
ADD_SUGGESTION "general" AS "What services do you offer?"

' Start the conversation
TALK "I have the latest announcements and company information ready."
TALK "What would you like to know?"

Creating LLM Tools

Instead of parsing user input, create tools the LLM can call:

' update-summary.bas - A tool the LLM can invoke
PARAM topic AS STRING LIKE "Q4 Results" DESCRIPTION "Topic to summarize"
PARAM length AS STRING LIKE "brief" DESCRIPTION "brief or detailed"

DESCRIPTION "Creates a summary of the requested topic"

' The tool logic is simple
data = GET_BOT_MEMORY(topic)
summary = LLM "Summarize this " + length + ": " + data
TALK summary

Execution Flow

Key Differences from Traditional Chatbots

Traditional Approach	LLM + BASIC Approach
Parse user input manually	LLM understands naturally
Complex IF/ELSE trees	Tools with PARAMs
Validate every field	LLM handles validation
Design conversation flows	LLM manages conversation
Handle errors explicitly	LLM provides graceful responses

Best Practices for LLM Era

Write Tools, Not Flows – Create reusable tools the LLM can invoke
Use Context Wisely – Load relevant knowledge with SET_CONTEXT
Trust the LLM – Don't micromanage conversation flow
Keep Tools Focused – Each tool should do one thing well
Use Suggestions – Guide users without forcing paths

Real-World Pattern

From production bots - a practical tool pattern:

' schedule-appointment.bas - A real business tool
PARAM service AS STRING LIKE "consultation" DESCRIPTION "Type of appointment"
PARAM date AS DATE LIKE "tomorrow at 3pm" DESCRIPTION "Preferred date and time"
PARAM notes AS STRING DESCRIPTION "Additional notes or requirements"

DESCRIPTION "Schedules an appointment and sends confirmation"

' Simple tool logic - LLM handles the conversation
appointment = GET "api/appointments/available" WITH service, date
IF appointment.available THEN
  SET_BOT_MEMORY "last_appointment" AS appointment.id
  SEND EMAIL TO user.email WITH appointment.details
  TALK "Perfect! I've scheduled your " + service + " for " + date
  TALK "Confirmation sent to your email"
ELSE
  TALK "That time isn't available. Let me suggest alternatives..."
  alternatives = GET "api/appointments/suggest" WITH service, date
  TALK alternatives
END IF

The LLM naturally guides the conversation, understands context like "tomorrow" or "next week", and calls this tool when all information is gathered.

Summary

BASIC in General Bots isn't about controlling conversation flow - it's about providing tools and context that LLMs can use intelligently. Write simple tools, let AI handle the complexity.

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