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Generative AI (GenAI) is a type of artificial intelligence that learns patterns from data and uses them to create new, derived content—such as text, images, audio, video, or code—in response to an input. It is broader than chatbots: a chatbot may be one application of a generative model, but generation can also happen without a conversational interface.
To understand what GenAI can and cannot do, it helps to separate the model that generates content from the larger product around it—and to know that plausible output is not automatically accurate.
What makes AI generative?
“Generative” describes the task: producing a new artifact based on learned patterns and an input or prompt. A model might produce a sentence, an image, a sound clip, a video sequence, or a code sample. The output is derived from patterns in data; it is not necessarily a copy of a specific item in that data.
The National Institute of Standards and Technology (NIST) defines generative AI as “the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content.” Its definition includes images, video, audio, text, and other digital content. IBM’s reader-facing definition similarly describes AI that can create original content in response to a prompt or request.
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That makes GenAI a category of AI, not a synonym for AI as a whole. A classifier might label a photograph as a dog, and a forecasting model might estimate next month’s demand. A generative model could instead create a new dog image or draft a written explanation. Real applications often combine these tasks: a product may retrieve documents, classify a request, generate an answer, apply safety filters, and send the result to a person for review.
How does generative AI work?
Most systems are built around a model trained to learn statistical relationships in data. During training, the model adjusts internal parameters to perform a prediction task; at use time, it produces an output conditioned on an input. The broad lifecycle includes pretraining, adaptation for a particular use, generation, evaluation, and ongoing improvement.
1. Pretraining teaches patterns
A foundation model is commonly trained on very large volumes of data, much of it raw, unstructured, and not individually labelled. In a self-supervised task, the model repeatedly predicts a missing or next element—for example, what token is likely to follow a sequence of text—and adjusts its parameters to reduce the difference between its prediction and the training target.
Repeated prediction helps the model encode statistical relationships in learned parameters and internal representations. Those parameters are not best understood as a searchable library of answers. They support generating likely continuations or other outputs in response to a particular input.
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2. Adaptation shapes a model for an application
A pretrained foundation model may be adapted through fine-tuning or instruction-tuning, aligned to encourage certain behaviours, connected to a retrieval system, or equipped with tools. These approaches serve different purposes. For example, retrieval can provide relevant information at request time; it does not mean the model’s underlying learned parameters have become a complete, up-to-date reference library.
A finished application may add more than a model: it can include a prompt, retrieved context, a user interface, tool access, safety controls, logging, and human review. A model’s capabilities and limits therefore do not, by themselves, describe every behaviour of the product that uses it.
3. Inference turns an input into an output
At runtime, a system converts the prompt and any attached context into a representation the model can process. A language model predicts a sequence of tokens—pieces of words, punctuation, or other symbols—one step at a time. Decoding settings influence how those predictions are selected, so the same prompt need not produce identical wording every time.
For a language model, this is a mechanism for producing a plausible continuation, not a guarantee that each sentence is a verified fact. For an image model, the generation process can be different: diffusion systems iteratively remove noise from a representation, guided by the prompt, until an image emerges. The model architecture, training, and application controls all shape what it can produce.
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4. Evaluation and monitoring continue after launch
Generation quality depends on the task and the conditions of use. A useful system needs evaluation suited to its actual setting, including checks for quality, safety, privacy, bias, and robustness. After deployment, monitoring can reveal failures or changes in how the system is used; evaluation and adjustment are not one-time steps.
NIST’s Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1, published in 2024) recommends governing, mapping, measuring, and managing risks across the AI lifecycle. NIST’s broader AI Risk Management Framework also describes characteristics to consider, including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness.
How do language and image models generate different things?
There is no single architecture behind every generative system. The model family and generation process depend on the data and output task.
| Model family or system | How it generates | Where it fits |
|---|---|---|
| Transformers, including GPT-style language models | Use attention to weigh relationships among elements in a sequence; language models predict output tokens conditioned on their context. | Transformers are the predominant architecture for large language models, according to NIST. They are widely associated with text generation and can also be part of systems that handle other modalities. |
| Diffusion models | Training adds noise to data, and the model learns to iteratively remove noise to form a desired output. | Used for image generation and in systems for other media. The denoising process supports fine-grained image generation. |
| Variational autoencoders and generative adversarial networks (GANs) | Use other approaches to learn data structure and produce new samples. | Important generative model families; their existence is one reason GenAI should not be reduced to large language models. |
| Multimodal foundation models | Work with, or generate, more than one modality, such as text and images or audio. | Capabilities depend on the model’s training, architecture, interface, and application controls; not every model supports every modality. |
The transformer architecture was published by Google researchers Ashish Vaswani and colleagues in 2017, a milestone IBM identifies in the development of modern transformer-based GenAI. That history does not mean every current generative model is a transformer: diffusion, autoencoder, and GAN approaches remain relevant to understanding the field.
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Depending on the model and the product built around it, GenAI can draft, transform, or summarize text; answer questions; write or explain code; generate or edit images; synthesize audio, music, or video; create synthetic data; and assist with research or workflow automation. These are examples of task categories, not a promise that one model can perform all of them.
- Text: A language model generates a sequence of tokens in response to a prompt and any context it receives.
- Images: A diffusion model can iteratively denoise a representation toward an image conditioned on a prompt.
- Audio, video, and code: Systems may generate these outputs using architectures and training objectives suited to the relevant modality or sequence.
- More than one modality: A multimodal model may accept or produce combinations such as text and images, but its actual supported inputs and outputs depend on the specific model and interface.
In practice, a workflow may combine a generative system with other software. For example, a developer might capture a webpage image for later inspection by a person or a multimodal AI system. The capture itself is not generative AI: it creates a screenshot of a page rather than generating new content from learned patterns. ScreenshotNeo is a website screenshot API and MCP server for developers; learn more at ScreenshotNeo.
A screenshot as a developer-workflow input
For a one-request webpage capture, replace the example URL with the page you want to capture and supply your API key. ScreenshotNeo returns an image or PDF; it does not, by itself, interpret the image or generate an AI description of it. Its API documentation is at ScreenshotNeo docs.
cURL:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpPython:
import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)Node.js:
const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);In that kind of workflow, a screenshot can be supplied as an image input only if the AI system used afterward accepts images. A text-only model cannot inspect visual page details merely because a screenshot exists, and the capture does not verify what an AI subsequently says about it.
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ScreenshotNeo captures a page with one API request. Cookie and consent banners are accepted like a visitor and more than 60 known consent platforms, newsletter popups, and chat widgets are removed before the shot; each step can be turned off. Bot checks and CAPTCHAs, blank pages, timeouts, failed loads, and cache hits cost nothing, with the response identifying the page verdict and billing status in headers. An MCP server offers take_screenshot, get_page_info, and capture_pdf tools for Claude, Cursor, and other MCP clients. The free plan includes 1,000 screenshots a month with no card; paid plans start at $5 for 3,000 screenshots.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is generative AI reliable?
Not by default. Fluent, specific-sounding output is not proof of factual accuracy. A model can produce an unsupported statement, omit an important qualification, or give different responses to similar prompts. That does not make every output useless; it means the required level of verification depends on the consequences of getting it wrong.
- Check consequential claims: Verify important facts against authoritative sources instead of treating generated prose as a citation.
- Test for bias and sameness: Training data, model design, and use can reproduce or amplify social and statistical bias, or lead to similarly framed outputs.
- Protect sensitive information: Consider what prompts, training data, outputs, and inferred attributes could reveal. Check the privacy and data-use conditions that apply to the specific service before entering sensitive material.
- Plan for security and misuse: Generated material can be used in fraud, social engineering, unsafe code, and other abuse. Access controls and monitoring matter in deployed systems.
- Review rights and provenance: Data rights, memorization, attribution, and disclosure of synthetic content can require domain-specific assessment.
- Account for safety and resource use: Test relevant failure modes and consider the resources needed to train and operate a system.
There is no single benchmark or vendor claim that establishes universal reliability. For a consequential use, evaluation should match the task, users, model version, and deployment conditions. NIST’s risk guidance calls for documented testing, evaluation, verification, and validation, together with ongoing risk tracking.
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Compare systems against the work they must do, not just the label “AI.” A tool that drafts marketing copy and a model used to support a high-stakes decision require different evidence and safeguards. Before adopting one, assess the following in the relevant product and deployment context:
- Modality and task coverage: Does the system accept and produce the kinds of inputs and outputs you actually need?
- Factuality, robustness, and control: How does it handle ambiguous prompts, unusual inputs, and requests for constrained output?
- Input limits and output quality: What context can it use, and does its output meet your task’s quality requirements?
- Latency, throughput, and cost: Can it respond within operational needs at the expected volume and cost?
- Privacy, data use, and security: What terms govern submitted data, retention, access, and abuse controls?
- Transparency and fairness: Can you assess provenance, explainability, and bias relevant to your users and setting?
- Integration and operations: Does it work with required tools and deployment locations, and can you monitor, audit, and govern it over time?
These questions align with NIST’s trustworthiness and lifecycle guidance. They are not a universal ranking: the appropriate choice depends on the task, risk, and conditions of use.
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