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Generative AI is a type of artificial intelligence that creates new content in response to instructions or other input. It can generate and transform text, images, audio, video, software code and other forms of information by learning patterns from large amounts of training data.
Tools such as ChatGPT, Microsoft Copilot and generative AI services in Microsoft Azure and AWS have made this technology accessible to people who do not build AI systems themselves. For organisations, the opportunity is significant, but useful adoption depends on understanding both what generative AI can do and where human judgement, data protection and governance are still essential.
What does generative AI mean?
Traditional software follows rules and instructions created explicitly by developers. Traditional machine learning often learns patterns in data to make predictions or classifications.
Generative AI goes further by producing new outputs based on patterns learned during training.
Depending on the model, it can generate:
- emails and reports
- summaries
- images
- presentations
- software code
- audio and speech
- video
- structured data
- answers to questions
How does generative AI work?
Generative AI models are trained on large collections of data so they learn statistical relationships and patterns.
A large language model, or LLM, is trained on text and related information. When given a prompt, it predicts and generates an appropriate sequence of tokens based on the context it has received and patterns learned during training.
This is why an LLM can produce fluent answers without storing every possible response as a pre-written document.
What is a large language model?
A large language model is an AI model designed to process and generate language.
Modern LLMs can perform many tasks through natural-language instructions, including:
- answering questions
- rewriting text
- translation
- summarisation
- classification
- brainstorming
- extracting information
- creating code
- reasoning across provided material
Some models are multimodal, meaning they can work with more than text, such as images, audio and documents.
What is a prompt?
A prompt is the instruction, question or context supplied to a generative AI system.
A weak prompt might be:
Write a report.
A stronger prompt provides useful context:
Draft a 500-word executive summary for a non-technical management audience. Explain the three main causes of the delay, quantify the business impact using the figures below and finish with three recommended actions.
Good prompting does not guarantee a correct answer, but it can greatly improve relevance and structure.
What is prompt engineering?
Prompt engineering is the practice of designing instructions and context so an AI model produces more useful output.
Useful techniques include:
- stating the objective clearly
- providing relevant background
- specifying the audience
- defining output format
- giving examples
- setting constraints
- asking the model to use supplied source material
- reviewing and refining the result
For everyday business use, prompt engineering is often less about clever tricks and more about giving clear instructions.
What is generative AI used for in business?
Writing and communication
Generative AI can help draft emails, proposals, policies, marketing material and internal communications.
Summarising information
Teams can use AI to summarise meetings, documents, research and long threads of information.
Research and idea generation
AI can help explore a topic, create initial options and identify questions that need further investigation.
Data analysis support
Generative AI can explain data, suggest formulas, help create queries and assist people in interpreting reports, provided the underlying data and output are checked.
Customer service
AI assistants can draft responses, retrieve knowledge and support agents handling customer enquiries.
Software development
Developers can use generative AI to explain code, generate examples, create tests, document systems and support debugging.
Training and learning
AI can provide explanations, generate practice questions and adapt examples to a learner's role or experience.
What is Microsoft Copilot?
Microsoft uses the Copilot name across AI-assisted experiences in products and services.
Microsoft 365 Copilot, for example, can work across business applications and organisational information according to the user's permissions and the product's configuration.
Copilot is an application of generative AI rather than a separate category of AI.
What is retrieval-augmented generation?
Retrieval-augmented generation, usually shortened to RAG, combines a generative model with relevant information retrieved from an external source.
For example, an organisation might create an AI assistant that searches approved policy documents before generating an answer.
RAG can help:
- ground responses in organisational information
- work with more current material
- reduce reliance on the model's general training knowledge
- provide source references in suitable applications
RAG does not guarantee accuracy. Retrieval quality, document quality and prompt design still matter.
What is fine-tuning?
Fine-tuning adapts a pretrained model using additional training examples for a particular task or behaviour.
It is different from simply adding documents through RAG.
RAG supplies relevant information at inference time. Fine-tuning changes aspects of the model's learned behaviour.
Organisations should choose the approach according to the problem rather than assuming every generative AI system needs fine-tuning.
What are AI agents?
AI agents extend generative AI by allowing a model to use tools, data sources or APIs and take a sequence of actions towards a goal.
An agent might:
- receive a request
- decide which information is needed
- query a system
- analyse the result
- perform an approved action
- report the outcome
Agents can create powerful automation opportunities, but the greater their ability to act, the more important permissions, monitoring and safeguards become.
What are the limitations of generative AI?
It can be confidently wrong
Generative models can produce incorrect or invented information that sounds convincing. This is often called hallucination.
It may not know current information
A model's training knowledge may not include recent events unless the system can access up-to-date sources.
It can reflect problems in data
Biases, gaps and poor-quality information can affect AI outputs.
It does not understand organisational context automatically
The model may not know internal policies, terminology or business rules unless suitable context is provided.
Outputs can vary
The same request can produce different responses, which matters where consistency and auditability are important.
What are the main business risks?
Important risks include:
- sharing confidential information with unapproved services
- incorrect output being treated as fact
- copyright and intellectual-property concerns
- biased or unfair decisions
- poor-quality automated customer communication
- over-reliance on AI without human review
- insecure integration with business systems
- unclear accountability
- staff using unsanctioned AI tools
NIST's Generative AI profile highlights that generative systems introduce risks that need to be managed throughout their lifecycle rather than treated as a one-off technical issue.
What information should employees avoid putting into public AI tools?
Organisations should define clear policies based on the tools they approve and the contracts and controls in place.
As a general principle, employees should not place confidential, personal, commercially sensitive or security-sensitive information into an AI service unless the organisation has explicitly approved that use and understands how the service handles the data.
The correct rules depend on the platform, configuration, legal requirements and organisational policy.
How can organisations adopt generative AI responsibly?
A practical approach includes:
- Identify genuine use cases. Start with business problems rather than buying AI because it is fashionable.
- Choose approved tools. Understand security, data handling and contractual terms.
- Define data rules. Make it clear what employees may and may not submit.
- Keep human oversight. Review outputs according to the risk involved.
- Train users. Staff need to understand prompting, verification, privacy and limitations.
- Measure value. Track whether AI actually saves time or improves outcomes.
- Create governance. Define ownership, risk assessment and escalation.
How should you verify AI output?
Verification should be proportionate to the impact of an error.
For low-risk brainstorming, a quick sense check may be enough.
For legal, financial, safety, HR, customer or technical decisions, users should verify claims against authoritative information and involve appropriately qualified people where required.
AI should not be treated as an unquestionable source.
Generative AI vs traditional AI
Generative AI is part of the wider AI field.
Other AI systems may classify images, predict equipment failure, detect fraud or recommend products without generating substantial new content.
See AI vs Machine Learning vs Deep Learning for the wider relationship.
Generative AI vs automation
Traditional automation follows defined rules and workflows.
Generative AI handles more open-ended language and content tasks.
The two can be combined. For example, a workflow might collect information automatically, ask an AI model to summarise it and then send the output for human approval.
Frequently asked questions
Is ChatGPT generative AI?
Yes. ChatGPT is a generative AI application built around large language models.
Is Microsoft Copilot generative AI?
Yes. Microsoft's Copilot products use generative AI capabilities in different business and technical contexts.
Will generative AI replace jobs?
AI is more likely to change the content of many roles than produce one uniform outcome across every occupation. Tasks involving drafting, research, analysis and administration are already changing, while human judgement, accountability and interpersonal skills remain important.
Can generative AI use company data?
Yes, when an approved solution is designed to access organisational information securely and according to permissions. The data architecture and governance are critical.
What is an AI hallucination?
It is an output that contains incorrect or fabricated information presented as though it were valid.
Do I need to learn programming to use generative AI?
No for many business applications. Technical development of AI applications and agents may require programming, APIs and cloud skills.
Develop your generative AI skills with ExperTrain
ExperTrain offers instructor-led Artificial Intelligence training for business users, leaders, developers and technical professionals.
For business users, Transform Business Workflows with Generative AI (AB-730) develops practical use of generative AI and Microsoft 365 Copilot.
Technical professionals can explore Develop Generative AI Apps in Azure (AI-3016). Beginners can start with Microsoft Azure AI Fundamentals (AI-900).
For related terminology, visit the ExperTrain Glossary Library.
Further reading
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