- AI
- AI Search
- AEO
What Is AI?
Updated
Artificial intelligence, or AI, is technology that lets computers perform tasks that normally require human intelligence. That includes recognizing images, understanding language, spotting patterns, making predictions, generating content, and helping make decisions.
You already use AI more often than you may realize. A spam filter decides which email looks suspicious. A maps app predicts traffic. A streaming service recommends what to watch next. A tool such as ChatGPT or Gemini can answer questions and create new content.
Those products look very different because AI is not one program. It is a broad field. Chatbots are only one part of it.
The easiest way to understand modern AI is to follow a simple chain:
- Data is used to train a model.
- The trained model receives new input.
- The model produces an output.
- A product can add search, files, memory, tools, and other information around that model.
This guide builds that picture one piece at a time.
Every one of these is AI. Only one of them looks like a chatbot.
What counts as artificial intelligence?
Artificial intelligence is a broad field of technologies that help computers perform tasks associated with intelligence. Depending on the system, that can mean recognizing an object, predicting an outcome, understanding language, generating an image, ranking information, or deciding what should happen next.
The important part is that AI is the category, not the product you open. ChatGPT, Gemini, Claude, Copilot, and Perplexity are products built with AI. A spam filter, recommendation system, fraud detector, and image-recognition model can also use AI even though none looks like a chatbot.
The AI terms worth knowing
| Term | Simple meaning |
|---|---|
| Artificial intelligence | The broad field. |
| Machine learning | A major way AI systems learn patterns from data. |
| Deep learning | A form of machine learning built with multilayer neural networks. |
| Generative AI | AI designed to create new output such as text, images, audio, video, or code. |
| Large language model | A type of model designed to work with language. |
| AI product | The app or service a person actually uses, often combining models with search, files, memory, tools, and an interface. |
You do not need to memorize the hierarchy. Keep one distinction in mind: AI is the broad field, and the tools you use are products built from parts of that field.
- Recognize and classify.Identify objects, patterns, messages, or categories.
- Predict and recommend.Estimate what may happen or what may be useful next.
- Generate.Create text, images, audio, video, code, or other output.
- Retrieve and organize information.Find useful information in documents, databases, or the web.
- Use tools and take actions.Interact with software when the product has been given that ability.
An AI assistant combines several of these. It is a product built from parts of the field — not the whole field.
What can AI do?
AI systems can perform several broad types of work:
- Recognize and classify. Identify objects, patterns, messages, or categories.
- Predict and recommend. Estimate what may happen or what may be useful next.
- Generate. Create text, images, audio, video, code, or other output.
- Retrieve and organize information. Find useful information in documents, databases, or the web.
- Use tools and take actions. Interact with software when the product has been given that ability.
These capabilities can overlap. An AI assistant might search the web, read a page, summarize it, and use another tool during the same task.
How AI learns
Much of modern AI is built with machine learning. Instead of writing a separate rule for every possible situation, developers train a model on examples so it can learn useful patterns and apply them to new information.
What is machine learning?
Machine learning is a way of building computer systems that learn patterns from data.
A spam filter is a simple example. Writing a rule for every possible spam message would be impossible. A machine-learning system can instead learn from many examples of spam and legitimate email, then use those patterns to classify a new message it has never seen before.
Machine learning is part of AI. It is not another name for the entire field.
What is training data?
Training data is the information used to help a model learn. It can include text, images, audio, video, code, numbers, labeled examples, or other data depending on the task.
The variety and quality of that data matter. A dog-recognition model trained only on bright photos of one breed may struggle when it sees a different breed in poor lighting.
Where can AI training data come from?
Training data can come from different places depending on the model. It may include licensed datasets, public or proprietary information, examples created or labeled by people, synthetic data created for training, and data collected through products or services under their policies.
Different AI companies and models use different mixtures of data, and the exact sources are not always fully public.
Training and retrieval are different. If information is used during training, it helps change the model. If an AI system retrieves a webpage later, the page is brought into the current task as outside information. A website being retrieved or cited in an AI answer does not mean the model was trained on that page.
How does AI training work?
A simplified training process looks like this:
- Give the model examples.
- Let it make a prediction or produce an output.
- Measure how well it did and adjust its internal parameters.
- Repeat until the model becomes more useful for the task.
Training does not turn the model into a database of exact examples. It changes the mathematical relationships inside the model.
Chapter 02 — How a model is trained
Watching a model get trained
Training data → Training → Model
New input → Model → Output
Confidence values shown here are illustrative.
- Examples enter
Labelled examples flow into training: photos that are dogs, photos that are not.
- The model guesses
The model makes a prediction. Early on it is barely better than a coin flip.
- Parameters adjust
Each wrong answer nudges the numbers inside the model. Repeat that across many examples and the guesses get better.
- New input
Training stops. A photo the model has never seen arrives, and the trained model classifies it.
Training changes the model. Inference uses the model.
What is an AI model?
An AI model is the trained mathematical system that remains after training. It can take new input and produce an output based on patterns learned during training.
That is different from a database. A database stores records so they can be looked up again. A model stores learned relationships across parameters. That is one reason asking a language model a factual question is not the same as retrieving an exact database record.
What is inference?
Once a model has been trained, using it on new information is generally called inference.
| Training | Inference |
|---|---|
| Changes the model | Uses the trained model |
| Adjusts model parameters | Processes new input |
| Happens while building or refining the model | Happens when the model is used |
| Can require enormous computing resources | Produces an output for a request |
Training changes the model. Inference uses the model.
When you ask an AI assistant a question, generate an image, or use an AI feature, you are usually seeing inference. The model is not normally retraining itself on your message in real time.
Does AI learn from everything you type?
Usually, not in the way people mean when they ask this question. Your current conversation can become context for later messages, and some products can save memories or use interactions according to their own policies. That is different from the underlying model instantly retraining itself every time you send a prompt.
Data-use policies differ by product, so privacy and training settings should be checked at the product level.
Where do neural networks and deep learning fit?
Many modern AI systems use neural networks, which are mathematical systems made of connected layers that transform information. Deep learning is machine learning that uses neural networks with many layers.
A useful hierarchy is:
Artificial intelligence → Machine learning → Deep learning
You do not need to understand the mathematics of neural networks to understand the rest of this guide.
Input layerHidden layerOutput layer
How generative AI works
What is generative AI?
Generative AI is AI designed to create new output from patterns learned during training. That output can include text, images, code, audio, video, or structured data.
Some modern systems are also multimodal, meaning they can work with more than one kind of information. A single assistant may be able to read text, inspect an image, analyze a document, and generate a response in the same conversation.
Predictive AI vs. generative AI
Predictive AI estimates or classifies something. Generative AI produces something new. The same product can use both.
| Predictive AI | Generative AI |
|---|---|
| Estimates whether a transaction looks fraudulent | Explains why it may have been flagged |
| Recommends a song | Creates a new song or description |
| Classifies an image | Creates a new image |
“Predictive” and “generative” describe what the system is doing, not two competing versions of AI.
Estimates whether a transaction looks fraudulent.
This transaction was flagged because it is unusually large, was made in a new location, and does not match this account's recent activity.
Explains why it may have been flagged.
What is a large language model?
A large language model, or LLM, is an AI model designed to work with language. LLMs can generate text, summarize documents, translate, classify information, extract details, write code, and support conversational products.
An LLM is the model, not the entire product. A product such as ChatGPT, Gemini, Claude, or Copilot can combine one or more models with an interface, files, memory, search, tools, and other systems.
What are AI tokens?
People read words and sentences. Language models process text in smaller units called tokens. Depending on the model, a token may be a whole word, part of a word, a space, punctuation, or another piece of text.
This is why terms such as token limit and context window appear when people discuss language models. They refer to how much tokenized information the model can work with at a time.
How does an LLM generate text?
A useful simplified explanation is that a language model generates text one token at a time.
Give it:
Peanut butter and…
“Jelly” may be a likely continuation. Other words could also make sense depending on the context. After a token is selected, the model repeats the process with the longer sequence. One small prediction at a time becomes a sentence, paragraph, code sample, or longer response.
Calling this “autocomplete” is useful only up to a point. Next-token prediction explains how text is produced, but modern language models learn relationships that support much more complex tasks than phone autocomplete.
- Pean
- ut
- space
- butt
- er
- space
- and
- jelly62%
- banana14%
- chocolate9%
- honey6%
- the4%
The candidates and percentages here are illustrative, not output from a real model or tokenizer. They show the shape of the process, not real values.
What is a prompt?
A prompt is the instruction or information you give a generative AI system.
A simple prompt might be:
Explain gravity.
A more useful prompt could be:
Explain gravity to a 10-year-old using a playground example. Do not use equations.
A prompt can include instructions, examples, pasted text, files, images, formatting requirements, and previous conversation context. That means the output depends on both the trained model and the information available during the current request.
Where AI gets its information
An AI response does not come from one single place. Depending on the product and request, the system may use information learned during training, information you provide, information it retrieves, or information returned by tools and connected services.
Four places information can come from
-
Training Patterns and relationships learned while the model was trained.
-
Your context Context is the information available to the model for the current request, including the prompt, earlier conversation, uploaded files, images, spreadsheets, or code.
-
Retrieved information Documents, databases, search indexes, websites, or other sources the system finds for the current request.
-
Tools and connected services Search, calculators, code environments, calendars, databases, or business software that can provide information or perform an operation.
The model is only one part of the product
This distinction makes modern AI products much easier to understand.
- Model: the trained mathematical system.
- AI system: the model plus the context, instructions, retrieval, tools, memory, and other components available for the task.
- Product: the experience a person actually opens and uses.
Memory is one of those components: information an AI product saves for future interactions when that feature is available and enabled. Memory is different from retraining the underlying model.
Two products can use similar models and still behave differently because the systems around those models are different.
Model
The trained mathematical system.
System
The model plus the context, instructions, retrieval, tools, memory, and other components available for the task.
Product
The experience a person actually opens and uses.
What is retrieval?
Retrieval means finding outside information that is relevant to the current request and making it available to the AI system.
Ask:
What happened in the market today?
A model cannot rely only on information learned before today. A search or retrieval system can find current information, select useful passages, and add them to the context for that request.
What is grounding?
Grounding broadly means connecting an AI response to outside information or evidence. A retrieved document, uploaded file, database, or web search can provide grounding.
Grounding can improve the information available to the system, but it does not guarantee a correct answer. The wrong source can be retrieved, the source can be wrong, or the AI can misread what the source says.
What is RAG?
Retrieval-augmented generation, or RAG, combines retrieval with generation. The system finds relevant information first, then gives that information to the model while it creates the response.
A useful analogy is an open-book test. The model still does the answering, but it can consult relevant material before it responds.
A simplified RAG flow is:
- A person asks a question.
- The system searches an information collection.
- Relevant passages are retrieved.
- Those passages become part of the current context.
- The model generates a response using the question and the retrieved information.
RAG is an important pattern, but it is not a universal blueprint for every product that uses search or outside information.
- Question
What happened in the market today?
- SearchSearch
- SourcesSource 1News reportSource 2Market dataSource 3Company filing
- Selected passagePassageFrom source 2
- AnswerAnswer
Why AI can be wrong
AI can produce an answer that is clear, specific, confident, and wrong. Good writing is not the same thing as good evidence.
What is an AI hallucination?
An AI hallucination is information generated by an AI system that sounds plausible but is incorrect, unsupported, misleading, or invented.
That can include:
- a made-up statistic
- the wrong date
- a quotation nobody said
- a study that does not exist
- a citation that does not support the claim
- a confident conclusion built from incomplete information
This becomes easier to understand once you remember that a generative model is producing an output from learned relationships and whatever context is available. It is not necessarily checking a perfect internal record before writing every sentence.
Why can AI sound confident and still be wrong?
AI can produce a clear, detailed answer even when part of the information is wrong. The writing style is not a reliability score.
Confidence is not verification. Fluency tells you how polished the answer sounds. It does not tell you whether every claim underneath it is true.
When did the fictional Northbridge Museum open?
The Northbridge Museum opened in 1987 and moved into its current building in 2004.
- Opened in 1987
SupportedSupported by the source.
- Moved in 2004
MismatchThe source says 2007.
Fluent does not mean verified.A sentence can sound completely normal even when the fact underneath it is wrong.
The Northbridge Museum is fictional. The dates are here to show what checking a claim looks like, not to state anything about the world.
How to check an important AI answer
- Identify the claim that matters. Focus on the date, number, quotation, recommendation, or other fact you would rely on.
- Open the original or cited source. Do not assume the citation proves the claim.
- Check that the source actually supports the claim and is current enough for the question.
If no source is provided, look for an original or authoritative source before relying on an important claim.
Other reasons AI gets things wrong
- Old information. Training data can become outdated.
- Weak or incomplete data. Missing or uneven examples can shape what a model learns.
- Bias. Bias can enter through data, labeling, objectives, system design, or deployment.
- Bad retrieval. A system can retrieve the wrong source or misunderstand a good one.
- Overreliance. People can trust an answer more than the evidence justifies.
Also remember that useful does not mean safe to share everything. Sensitive information should only be provided to systems whose privacy and data-use policies you understand.
How AI search works
You have already seen the core retrieval pattern: ask a question, find useful information, add that information to the model’s context, and generate a response. AI search applies that same pattern across the web, often across several related information needs at once.
AI search combines search and AI to help interpret a question, find relevant information, and organize or generate a response.
Different products use different systems, but a useful general pattern is:
Understand the question → find useful sources → select relevant information → use that information to build a response.
What is AI search?
AI search is a broad term for search experiences that use artificial intelligence to interpret a request, retrieve useful information, and present a more direct or synthesized response.
Google Search can use generative experiences such as AI Overviews and AI Mode. Perplexity is built heavily around web retrieval and citations. ChatGPT can search the web inside a conversational experience. Microsoft Copilot can use web search in supported experiences.
The products differ. The common idea is that search, retrieval, and generation can work together instead of existing as separate steps for the user.
Traditional search vs. AI-assisted search
Traditional search
- Search for a query.
- Open useful results.
- Compare sources.
- Build your own answer.
AI-assisted search
- Ask a question.
- The system may explore several related information needs.
- Search or retrieval finds useful sources and passages.
- The AI organizes or generates an answer, sometimes with citations.
These experiences overlap. Sometimes the best result is a direct answer. Sometimes it is still a webpage, product, map, forum discussion, video, or original source.
One question can contain several smaller questions
Consider:
What should I look for when buying my first electric car?
That is one sentence, but it contains several information needs: range, charging, purchase price, battery warranty, reliability, incentives, and everyday ownership.
An AI search system can break the larger question into related searches, retrieve different sources for different needs, and combine useful pieces into one response.
This kind of branching is often described as query fan-out: one question can lead to several related searches behind the scenes.
Chapter 08 — One question, several searches, one answer
How an AI search answer gets built
The question about buying a first electric car expands into several information needs, including range, charging, cost, reliability and incentives. The system searches six sources of different types and keeps three of them, adds those passages to the AI system's context, and builds an answer whose lines carry numbered citations — 1, 2 and 3 — pointing back to the three sources it used.
Conceptual illustration. The sources and answer shown are placeholders, not real results.- Question
One question goes in: “What should I look for when buying my first electric car?”
- Fan out
Behind the scenes it can become several related searches. This branching is often described as query fan-out.
- Retrieve
Each branch finds sources. Only the passages that actually help move forward.
- Answer
The selected passages are combined into one response, with citations pointing back to where each part came from.
Why citations matter
A citation gives the reader a path from the generated answer back to a source. It can help verify a claim, inspect context, compare perspectives, or continue researching.
A citation is not a guarantee that the claim is correct. It makes part of the evidence chain visible.
How websites become part of AI answers
So far, we have followed a person’s question toward an answer. Turn the process around and a different question appears: what makes information on a webpage useful enough to be retrieved and cited?
A website can reach an AI system in more than one way. Earlier we looked at information that may influence a model during training. This section is about the other path: information being discovered and retrieved for a current request.
There is no single ranking formula shared by every AI product, but several fundamentals are consistent with good search and publishing practice.
1. The information has to be accessible
A system cannot use content it cannot reach. Crawl restrictions, authentication, rendering problems, or network blocks can prevent a page from being retrieved before content quality is even considered.
2. The page has to be relevant
A strong page still needs to answer the question being asked. Publishing more content does not make a page relevant to more questions.
3. The writing has to be clear
Descriptive headings, direct explanations, useful context, and accessible text make it easier for people and systems to understand what a passage means.
4. The content has to be worth using
Original information, firsthand experience, clear evidence, useful examples, and expert analysis give a system a reason to prefer one source over a page that simply repeats common knowledge.
5. The useful passage has to be retrievable
AI search often works with specific passages, not just whole pages. The answer to an important question should not be buried under unnecessary setup or dependent on an animation that has no readable text equivalent.
If the product surfaces citations, the source also needs to be available as a destination the user can open and inspect.
The page exists and publishes something worth using.
A system cannot use content it cannot reach. Crawl restrictions, authentication, rendering problems, or network blocks stop a page before quality is even considered.
AI search often works with specific passages, not just whole pages.
The passage is found and made available to the AI system for that request.
The retrieved information is used while the response is generated.
A citation sends the reader back to the original source as a destination they can open and inspect.
What does AI search mean for SEO?
Search engine optimization, or SEO, is the work of improving how useful webpages can be discovered, understood, and surfaced in search. The fundamentals overlap heavily. Search systems still need to discover and access the page. People still need useful information. Relevance, credibility, internal linking, clear structure, and page experience still matter.
AI search adds another route for that information to surface. A passage can help support a generated answer, and a citation can send the reader back to the original source.
That makes the question broader than “Did this page rank for this keyword?” It also becomes:
- Does the page clearly answer what a person is trying to understand?
- Can the useful information be found and retrieved?
- Does the passage still make sense when read outside the rest of the article?
- Does the source provide something more useful than interchangeable summaries?
Where does AEO fit?
I use answer engine optimization, or AEO, to describe work that improves how information can be discovered, understood, retrieved, and used in answer-based experiences.
AEO does not replace SEO. The two share the same foundation: technically accessible pages, useful content, clear information architecture, credible sourcing, strong topical relevance, internal linking, and a good experience for the person using the page.
The interface is changing. The need to publish information worth finding is not.
If you want the practical version of that work, read Answer Engine Optimization (AEO): The Complete Guide.
What can AI systems do beyond answering questions?
AI systems increasingly do more than produce one response. Some can search for information, use software tools, work with files, run code, or continue through several steps toward a goal.
Systems that can plan and use tools across multiple steps are often described as AI agents or agentic systems. The exact capabilities vary by product, but the important shift is from only producing an answer to also being able to take actions.
When you encounter a new AI product, ask:
- What is the model being asked to do?
- What information does the system have right now?
- What came from training and what came from current context?
- Can it retrieve outside information?
- Can it use tools or take actions?
- How can the result be checked?
Those questions are more useful than memorizing the feature list of any one product.
Data can train a model. The model processes new input. Modern products add context, retrieval, search, memory, and tools around the model — and the result still needs judgment.
The five things to remember
- AI is a broad field. Chatbots are one application of it.
- Machine learning trains models to learn patterns from data. Training changes the model, and inference uses it.
- Generative AI creates new output. LLMs are models built to work with language.
- The model is only one part of a modern AI product. Context, retrieval, memory, search, and tools can add information and capabilities around it.
- AI output still needs judgment. A polished answer can be wrong, so important claims should be checked against reliable evidence.
If you understand those five ideas, you already have the foundation needed to make sense of most AI products you encounter.
For a closer look at systems that plan and use tools across several steps, read What Is an AI Agent?
Common questions about AI
Is AI the same thing as ChatGPT?
No. AI is the broad field. ChatGPT is one product built with AI. Recommendation systems, fraud detection, computer vision, Gemini, Claude, Copilot, Perplexity, and many other technologies can also use AI.
What is the difference between AI and machine learning?
Artificial intelligence is the broader field. Machine learning is a major approach within AI that allows systems to learn useful patterns from data.
Why can two AI assistants give different answers?
They may use different models, instructions, context, retrieval systems, tools, or information sources. Even similar products can therefore behave differently.
Is AI the same as automation?
No. Automation means using technology to perform work with less manual intervention. Automation can use AI, but many automated processes simply follow predefined rules and do not need AI at all.