Generative AI is a category of artificial intelligence that creates new content based on patterns learned from large amounts of data.

It can generate:

Text
Images
Code
Audio
Video
Structured responses

Modern generative AI is commonly powered by foundation models such as large language models and multimodal models.

What Is Generative AI?

Generative AI produces new output rather than only classifying, ranking, or predicting existing data.

For example, a generative AI system can:

Write an answer
Summarize a document
Generate code
Translate text
Create an image
Rewrite content
Continue a conversation

The generated result is based on the user's input, the model's learned patterns, and any context or external information provided to the system.

Generative AI Is Not the Same as GANs

Generative AI is sometimes incorrectly described as another name for Generative Adversarial Networks, or GANs.

That is not correct.

GANs are one type of generative model architecture.

Generative AI is the much broader category that includes systems based on:

Large language models
Transformer architectures
Diffusion models
Variational autoencoders
GANs
Other generative model families

Today, most conversational generative AI systems are powered by large language models rather than GANs.

How Generative AI Works

A generative AI model learns statistical patterns from training data.

When it receives an input, such as a prompt or image, it generates a response based on those learned patterns.

In a production system, the model may also use:

System instructions
Conversation context
Business information
Search results
Tool outputs
Structured data
Workflow state

This additional context helps make the response more useful for a specific task.

Generative AI and Large Language Models

A Large Language Model is a type of generative AI model designed to understand and generate language.

LLMs can support tasks such as:

Question answering
Summarization
Translation
Classification
Information extraction
Content generation
Conversation

Not all generative AI systems are language models, but LLMs are one of the most important forms of generative AI today.

Generative AI vs. Traditional AI

Traditional AI systems often focus on tasks such as:

Classification
Prediction
Detection
Ranking
Recommendation

Generative AI creates new output.

For example:

Traditional AI: Classify a support request as billing.

Generative AI: Write a grounded response explaining the billing policy.

Many modern AI systems combine both approaches.

Generative AI vs. Conversational AI

Conversational AI focuses on interacting with users through natural-language conversation.

Generative AI is the underlying content-generation capability that can power that interaction.

A conversational system may also include:

Search
Routing
Workflow logic
Tools
Human Handoff

Generative AI is therefore one component of a broader conversational system.

Generative AI and AI Agents

An AI Agent uses generative AI as part of a larger system that can understand a goal, access information, use tools, and complete tasks.

Generative AI may handle:

Language understanding
Response generation
Reasoning
Summarization

The agent architecture adds capabilities such as:

Routing
Search
Tools
Skills
Workflow logic
Memory or state
Human Handoff

This distinction is important because generating an answer is not the same as completing a workflow.

Generative AI and Grounding

A model can generate fluent answers even when it does not have reliable information.

Grounding connects the response to relevant source information.

Grounding can use:

Direct context
Document Search
Data Search
APIs
Tools
Live sources

This helps reduce unsupported or outdated responses.

Generative AI and Search

Generative AI often works together with search.

Search retrieves relevant information.

Generative AI uses that information to form a natural-language response.

For example:

User asks about a company policy.
Search retrieves the relevant section.
Generative AI explains the policy in plain language.

This is especially useful for customer support and internal knowledge workflows.

Generative AI and Structured Data

Generative AI can also work with structured information.

Examples include:

Customer records
Product catalogs
Pricing data
Appointment availability
Order status

The model can interpret the user's question while the system retrieves the exact data needed to answer it.

Generative AI and Tool Use

Generative AI becomes more useful when it can access tools.

Examples include:

Scheduling
Calculations
Live web search
CRM lookup
Product search

Tool Calling allows the model or agent to request these capabilities when needed.

Common Generative AI Use Cases

Customer Support

Generate answers from policies, help content, and customer information.

Customer Service

Handle questions, service requests, booking, and general assistance.

Sales

Answer product questions, qualify leads, and guide prospects toward the next step.

Internal Support

Help employees search company information and complete internal workflows.

Document Assistance

Summarize, explain, and extract information from documents.

Data Interaction

Turn natural-language questions into useful answers based on structured business data.

Generative AI Limitations

Generative AI is powerful, but it is not inherently reliable.

Common limitations include:

Hallucinations
Outdated information
Missing context
Ambiguous instructions
Inconsistent outputs
Weak source selection
Incorrect tool use

These limitations are why production AI systems often combine generative models with grounding, guardrails, routing, workflow logic, and human oversight.

Generative AI and Hallucinations

An AI Hallucination occurs when a model produces information that is unsupported, fabricated, or incorrect.

A fluent response can still be wrong.

Grounding and source-based workflows help reduce this risk.

Generative AI and AI Response Oversight

AI Response Oversight helps teams review how generative AI behaves in real interactions.

Oversight can identify:

Incorrect answers
Weak source use
Poor tone
Missing escalation
Repeated failure patterns

This is important when generative AI is used directly with customers.

Generative AI in AskHandle

AskHandle uses generative AI as part of a broader AI agent architecture.

AI Answer handles generative responses and can work with direct context of up to 200,000 words.

Other parts of the workflow can handle:

Document Search
Data Search
Routing
Question Flow
Human Handoff

AI Answer can also use enabled skills such as Scheduling, Live Web Search, Calculations, Product Cards, Image Answers, and Albums.

This allows generative AI to operate inside a controlled workflow rather than as a standalone text generator.