What Is Generative AI? How It Works, Helps and Fails

Generative AI creates new content such as text, images, audio and code. This guide explains how it works, where organizations use it, the risks that NIST identifies, and how to use it responsibly.

What is generative AI?

NIST's generative AI profile (July 2024) quotes the US executive order definition: generative AI is "the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content. This can include images, videos, audio, text, and other digital content." In everyday use, the term mostly refers to large language models and image generators.

Key takeaways

• Generative AI produces new content from patterns learned in its training data.
• It is a subset of deep learning and builds on the Transformer architecture for language.
• It is fluent, which is not the same as being correct: it can produce confident errors.
• Grounding it in your own documents and keeping people in the loop reduces, but does not remove, the risk.
• Governance should be set up before use spreads, not after.

How does generative AI work?

• Pretraining: a large neural network learns patterns from very large amounts of data. For a language model, the core task is predicting the next token in a sequence.
• Adaptation: the model can be adjusted for a purpose, for example with fine-tuning.
• Prompting: the user or application gives instructions and context; see prompt engineering.
• Grounding: the application can retrieve relevant documents and give them to the model, an approach called retrieval-augmented generation (RAG).

The foundations are covered in What Is Machine Learning? and What Is Deep Learning?.

How is generative AI used?

• Drafting and editing: first drafts, rewriting, tone and translation, followed by human review.
• Summarizing: condensing long documents, meetings or support histories.
• Question answering over your own content: a grounded assistant that answers from approved documents (see How to Build a RAG Pipeline).
• Code assistance: suggesting and explaining code for developers to review.
• Customer support: drafting answers and routing requests.
• Workflows: deciding what should happen next and coordinating tools (see What Is Flow AI?).

Business use is broad: the Stanford AI Index reports that 78% of organizations said they used AI in 2024, up from 55% the year before. That figure covers AI in general, not only generative AI.

What are the risks of generative AI?

NIST's profile lists risks that are unique to or made worse by generative AI. Among them:

• Confabulation: "the production of confidently stated but erroneous or false content."
• Data privacy: leakage and unauthorized use or disclosure of personal data.
• Harmful bias or homogenization: amplification of historical and societal biases.
• Information integrity: a lower barrier to producing and spreading false or misleading content.
• Intellectual property: eased production or replication of copyrighted or trademarked content.
• Human-AI configuration: risks from how people and AI systems work together, such as over-reliance.
• Environmental impacts: the compute used in training and running models.
• Value chain and component integration: non-transparent or untraceable use of third-party components.

How do you use generative AI responsibly?

• Ground answers in approved documents and show the source.
• Keep a person responsible for outputs that affect people, and make review easy.
• Test for errors and bias on realistic examples before launch, and keep testing.
• Limit what data and tools the system can reach, and log what it does.
• Tell people when they are dealing with AI-generated content.
• Write down who owns each system, its purpose and its limits (see Ethical AI).

What should you ask before buying or building?

• Which model is used, where does it run, and where does our data go?
• How is it grounded, and what happens when it does not know?
• How is accuracy measured, and how often?
• Who can see prompts and outputs, and how long are they kept?
• Can it be switched off, and how is data removed?

Where Zunkiree Labs fits

Zunkiree Labs builds custom AI systems (including RAG pipelines, LLM integration and intelligent automation), data systems, custom software, and web and mobile applications. This includes grounded assistants built on your own documents, and orchestration: Orca is Zunkiree Labs' intelligence and orchestration layer: it sits above the CRM, email and marketing tools an organization already uses and coordinates agent workflows across them, rather than replacing those tools. Orca is being provided to support use cases in education businesses and hospitals. The full list is on the services page, and the AI Development service covers custom AI systems in more detail.

Keep reading

• What Is Machine Learning? — the foundation: how systems learn from data, with NLP, neural networks and LLMs explained
• What Is Deep Learning? — how neural networks learn, and when they are the right tool
• Ethical AI — principles, risks and how organizations apply them
• How to Build a RAG Pipeline — a step-by-step engineering guide
• What Is Flow AI? — AI applied to workflows

Sources

• NIST AI 600-1: Artificial Intelligence Risk Management Framework, Generative AI Profile (July 2024)
• Stanford HAI: 2025 AI Index Report
• Vaswani et al., Attention Is All You Need (arXiv, 2017)