What is AI?

This guide provides a foundational understanding of artificial intelligence, specifically focusing on the type of AI that powers modern chatbots like the ones you can build with FutureBase.

Artificial Intelligence: The Basics

Artificial Intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence. These tasks include understanding language, recognizing patterns, making decisions, and generating responses.

AI is not a single technology but an umbrella term covering many different approaches:

How Large Language Models Work

LLMs are the engines behind modern AI chatbots. Here's a simplified explanation of how they function:

Training Phase

1. Data collection: The model is exposed to enormous amounts of text data (books, websites, articles, conversations)
2. Pattern learning: Through training, the model learns statistical relationships between words, concepts, and ideas
3. Parameter storage: These learned patterns are stored as billions of numerical parameters (weights)

The model doesn't "memorize" specific facts like a database. Instead, it learns patterns of how language works and how concepts relate to each other.

Inference Phase (When You Chat)

When you ask a question:

1. Input processing: Your message is converted into numerical tokens the model can understand
2. Context consideration: The model considers your question along with any conversation history
3. Probability calculation: For each possible next word, the model calculates how likely it is to follow
4. Response generation: The model selects words one at a time, building a coherent response

Key Concepts to Understand

Context Window

The context window is the amount of text an LLM can consider at once. This includes:

• The conversation history
• Any retrieved information (like content from your knowledge base)
• System instructions

Modern LLMs have context windows ranging from 8,000 to 200,000+ tokens (roughly 6,000 to 150,000 words). Longer context windows allow for more nuanced conversations but have limits.

Retrieval-Augmented Generation (RAG)

Your chatbot uses RAG to provide accurate, specific answers about your business:

1. User asks a question → "What's your return policy?"
2. System searches your knowledge base → Finds relevant content from your website/FAQs
3. LLM receives question + relevant content → Generates a response based on your actual data
4. User receives an informed answer → Based on your specific policies, not generic information

This is why content quality matters so much — the LLM can only be as accurate as the information it retrieves.

Tokens

Tokens are the units LLMs use to process text. A token is roughly:

• 4 characters in English
• 0.75 words on average

Understanding tokens helps you grasp:

• Why very long conversations might lose context
• How pricing works for AI services
• Why concise, clear content performs better

Temperature and Randomness

LLMs have a temperature setting that controls randomness:

• Low temperature (0.0-0.3): More predictable, consistent responses
• High temperature (0.7-1.0): More creative, varied responses

For customer support chatbots, lower temperatures are typically preferred to ensure consistent, reliable answers.

What AI Chatbots Can Do

Modern AI chatbots excel at:

• Understanding natural language: Interpreting questions even when phrased differently
• Generating human-like responses: Writing clear, grammatically correct text
• Following instructions: Adhering to guidelines about tone, format, and boundaries
• Synthesizing information: Combining multiple pieces of content into coherent answers
• Maintaining context: Remembering previous messages in a conversation
• Handling variations: Answering the same question asked in many different ways

What AI Chatbots Cannot Do

It's equally important to understand limitations:

• Access real-time information: Without integrations, AI doesn't know current events or live data
• Truly understand: AI simulates understanding through pattern matching, not genuine comprehension
• Guarantee accuracy: AI can confidently state incorrect information (hallucinations)
• Learn from conversations: Most chatbots don't retain information between separate sessions
• Make complex judgments: Nuanced decisions requiring human empathy or ethics need human oversight

For a detailed discussion of limitations, see AI Limitations & Considerations.

The Role of Your Knowledge Base

Your chatbot's effectiveness depends heavily on the knowledge base you provide:

User Question
     ↓
┌─────────────────────┐
│  Your Knowledge     │
│  Base Content       │ ← FAQs, website content, documents
└─────────────────────┘
     ↓
┌─────────────────────┐
│  LLM Processing     │ ← Generates response using your content
└─────────────────────┘
     ↓
Accurate, Contextual Answer

Without quality content, the LLM has to rely on its general training data, which may not reflect your specific business, policies, or products.

AI vs. Traditional Chatbots

Traditional chatbots (rule-based or decision-tree bots) work differently:

AI chatbots offer more natural conversations but require different management approaches, particularly around content quality and monitoring.

Summary

• AI chatbots use Large Language Models trained on vast text data
• They predict probable responses rather than retrieving pre-written answers
• Your knowledge base provides the specific information needed for accurate responses
• Understanding both capabilities and limitations helps you set appropriate expectations
• Content quality directly impacts response quality

Next Steps

• Learn how to use AI effectively for best results
• Understand AI limitations and considerations to set proper expectations
• Review improving content quality to optimize your chatbot's accuracy