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AI is the umbrella term. Machine learning is one way to build AI; deep learning is a machine-learning approach; generative AI describes systems that produce content; an LLM is a language-focused model; retrieval, fine-tuning, and prompting are different ways to change what a model does at runtime or after pre-training. An AI agent adds goal-directed action and tool use, while AGI remains a contested label rather than a verified description of a current product.
Keeping those boundaries straight makes product claims easier to evaluate. The same interface can combine a pre-trained model, retrieval, external tools, safety rules, and an action loop, but those components are not interchangeable and do not provide the same guarantees.
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The basic map: AI, machine learning and deep learning
Artificial intelligence is the broad category
Artificial intelligence (AI) covers artificial systems that perform tasks associated with human-defined objectives, such as making predictions, recommendations, or decisions. NIST’s AI glossary describes an AI system as “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments.” That definition does not require a system to think, feel, understand language like a person, or use machine learning.
A rules engine that applies explicit conditions can therefore be an AI system even though no model learned from examples. So can a vision classifier, a planning program, or a recommendation service. The useful question is not whether a product has an “AI” label, but what method it uses and what it can actually do.
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Machine learning is a way to build AI
Machine learning (ML) is a subfield of AI in which a system learns patterns from data instead of receiving every rule explicitly. During training, an algorithm adjusts parameters so its outputs better match examples or an objective. At runtime, the trained system uses those learned parameters to produce a prediction, classification, ranking, or other result.
AI and ML are therefore not perfect synonyms. ML is a major approach inside the larger AI field, but an AI application can also rely on hand-written rules, search, optimization, or a combination of methods.
Deep learning means multilayer neural networks
Deep learning is a machine-learning approach based on neural networks with multiple layers. The layers learn increasingly useful representations from data, which makes deep learning effective for language, speech, images, video, and other complex inputs. Deep learning is not synonymous with all AI, and a deep-learning model does not have to generate content. A model that identifies objects in photographs can be deep learning without being generative.
| Term | Scope | What is learned or specified | Typical output |
|---|---|---|---|
| Artificial intelligence | Broad field | May use rules, search, optimization, or learned models | Predictions, recommendations, decisions, or actions |
| Machine learning | Subfield of AI | Patterns learned from data | Predictions, classifications, rankings, or generated responses |
| Deep learning | Approach within ML | Representations learned by multilayer neural networks | Recognition, prediction, generation, or control |
Generative AI, LLMs and foundation models
Generative AI describes content production
Generative AI (GenAI) refers to models or capabilities that generate content from learned patterns and an input. The content can be text, images, audio, video, or code. “Generative” describes what the system produces; it does not mean the system is conscious, has human creativity, or is always correct.
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Image, music, speech, and video generators can all be generative without being language models. Conversely, a language model can be used for classification or extraction instead of free-form generation. The modality and task matter more than the marketing label.
An LLM is language-focused
A large language model (LLM) is trained to process and generate text, usually by learning statistical relationships in very large language datasets. An LLM can answer questions, summarize, translate, classify, or write code, but fluent text is not proof that it has verified facts, browsed the web, or understood a situation as a human would.
Foundation model describes reuse and adaptability
“Foundation model” describes broad pre-training and the ability to adapt a model to many downstream tasks. NIST defines a foundation model in generative AI as “models trained on broad data using self-supervised learning that can be adapted such as through fine-tuning for a variety of downstream tasks.” The label is not limited to language: foundation models can work with images, audio, video, or multiple modalities. An LLM can be a foundation model, but the two terms describe different properties—language modality versus broad pre-training and adaptability.
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| Question | Generative AI | LLM | Foundation model |
|---|---|---|---|
| What does the term emphasize? | Producing content | Language input and output | Broad pre-training and adaptation |
| Can it cover images or audio? | Yes | Not by the term alone | Yes |
| Must it generate text? | No | It processes and generates text | No |
Prompts, prompt engineering, training and inference
A prompt is the runtime input
A prompt is the input supplied to a generative model. It may be a question, instruction, role, example, formatting requirement, or other conditioning information. The model’s response is the output. Adding a longer prompt does not rewrite the model’s parameters; it changes the information presented for that particular request.
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Prompt engineering changes the input, not the model
Google for Developers defines prompt engineering as “The art of creating prompts that elicit the desired responses from a large language model.” In practice, prompt engineering can specify the task, audience, constraints, examples, output format, and criteria for uncertainty. It is useful when you need more consistent behavior from an existing model, but it is not retraining, fine-tuning, or adding a private database.
Training, pre-training and inference happen at different times
- Training: model parameters are adjusted using data and an optimization objective.
- Pre-training: the broad initial training phase that gives a model general capabilities.
- Inference: runtime use of the trained model to produce an output from an input.
When you send a prompt, you normally trigger inference, not training. The model may use the current conversation as context, but that temporary context should not be confused with changing its underlying parameters.
Fine-tuning is additional training
Fine-tuning starts with a pre-trained model and trains it further on task- or domain-specific information. It can make a model follow a particular output style or perform a specialized task more consistently. Fine-tuning changes parameters through additional training; it is neither prompt wording nor retrieval.
| Method | When it happens | What changes | What it is not |
|---|---|---|---|
| Prompting | At each inference request | The input context and instructions | Retraining the model |
| Fine-tuning | Before deployment or in a later training run | Model parameters | A prompt or a document lookup |
| Inference | When the user or application calls the model | The generated result for that request | Learning new parameters by default |
What RAG does—and what it cannot guarantee
Retrieval-augmented generation (RAG) is a pipeline that retrieves relevant documents or records after a model has been trained, places that material into the model’s input, and asks the model to generate an answer grounded in the retrieved context. It can supply newer information, private company material, or citations without changing the base model’s parameters.
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A typical RAG flow
- Prepare sources: documents are collected, cleaned, divided into passages, and indexed.
- Retrieve: a user question is matched to potentially relevant passages using keyword, vector, or hybrid search.
- Assemble context: selected passages and instructions are inserted into the model’s prompt.
- Generate: the model writes an answer using that context.
- Check: the application can show citations, apply rules, or send the result for human review.
RAG is not a database by itself; it is a retrieval-plus-generation design. It also is not a truth guarantee. If retrieval misses the right passage, returns an outdated or conflicting source, or supplies low-quality text, the generated answer can still be wrong. The model may misread a passage or make an unsupported claim despite having retrieved evidence. Source quality, retrieval quality, context limits, and verification all matter.
Hallucinations: a confident answer can still be false
In practical AI usage, a hallucination is a generated answer that sounds confident or plausible but is false, unsupported, or fabricated. The term describes a model-output failure; it is not evidence that the model perceived something or had a human-like experience.
Why hallucinations happen
- The model is generating likely continuations rather than consulting a guaranteed truth store.
- The prompt leaves key facts, dates, units, or definitions ambiguous.
- Training data contains errors, gaps, or conflicting accounts.
- Retrieval returns incomplete, irrelevant, or outdated material.
- The model is pressured to answer even when it lacks enough evidence.
How to reduce the risk
- State the date, geography, edition, audience, and required level of precision.
- Provide authoritative source material or use a retrieval system that exposes the passages it used.
- Ask the system to distinguish verified facts, inferences, and uncertainty.
- Check names, quotations, calculations, legal or medical claims, and any decision with real consequences.
- Use a human reviewer when an incorrect answer could cause material harm.
A citation displayed by an application is useful only when it actually supports the claim. Retrieval and citations improve auditability, but neither makes every generated sentence true.
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An AI agent is organized around a goal and can decide on steps or actions, sometimes by calling tools or operating in an external environment. A simple chatbot that returns one response to one prompt may not be agentic. The word “agent” should describe an action loop rather than serve as a grade for intelligence.
The four questions that reveal whether a system is agentic
- Goal: Is the system pursuing a defined outcome over multiple steps?
- Autonomy: Can it choose the next step without a new instruction for every action?
- Tools and environment: Can it call APIs, search records, run code, send messages, or change external state?
- Controls: Are permissions, approval gates, memory limits, logging, and stop conditions defined?
A workflow that drafts an email for approval has less action authority than one that sends it automatically. Both may use the same underlying LLM. The product’s interface and orchestration determine what the system can do, while the model supplies one component of the decision or generation process.
| System type | Typical runtime grounding | Action authority | Evaluation target |
|---|---|---|---|
| Answer-only model | Prompt context | Returns a response | Accuracy, factuality, latency, safety |
| RAG application | Retrieved documents | Returns a grounded response | Retrieval quality and factuality |
| Tool-using assistant | Retrieval plus external tools | Can request or perform defined tool calls | Task success, tool correctness, safety |
| Agentic workflow | Tools, memory, and an action loop | Can pursue a goal across steps | Task success, reliability, latency, safety, and containment |
AGI is not a synonym for a powerful chatbot
Artificial general intelligence (AGI) is a contested, future-oriented label for broad capability across many tasks. Different writers use different thresholds: some emphasize transfer to unfamiliar problems, others autonomy, learning efficiency, reliability, or performance comparable to people across domains.
Because there is no universally accepted test in the terminology covered here, calling a current product “AGI” without defining the threshold is not a verifiable technical conclusion. A polished conversation, a high benchmark score, or a tool-using workflow does not by itself establish general intelligence. Ask what capabilities are claimed, under which conditions they were measured, and what failures remain.
Bias, fairness, explainability and alignment are different questions
These terms describe properties or goals, not interchangeable synonyms for intelligence or accuracy.
- Bias: systematic effects in data, modeling, or use that can produce different or undesirable outcomes.
- Fairness: whether outcomes or treatment meet a chosen fairness criterion for the relevant people and context.
- Explainability: how well a system’s behavior or output can be described or examined.
- Alignment: whether behavior matches intended objectives, instructions, policies, or human values as defined for the application.
A system can be accurate on an aggregate test and still produce unfair outcomes for a subgroup. It can be explainable without being safe, or aligned with a narrow instruction while failing a broader goal. NIST’s terminology work emphasizes that these words have context-dependent meanings, so evaluations should name the specific metric and population rather than treating any one label as a complete safety claim.
A practical way to decode an AI claim
When a vendor or colleague says a feature is “AI-powered,” separate the claim along six axes:
- Modality: Does it handle text, images, audio, video, code, or several kinds of data?
- Model scope: Is it a narrow model, an LLM, or a broader foundation model?
- Training stage: Is the model pre-trained, fine-tuned, or both?
- Runtime grounding: Does it use only the prompt, retrieve documents, browse, or call tools?
- Action authority: Does it answer only, request approval, or change external systems?
- Evaluation target: Was it measured for accuracy, factuality, latency, safety, or end-to-end task success?
This checklist prevents category errors. For example, a fine-tuned language model is not automatically a RAG system; a browsing feature is not automatically an autonomous agent; and a generative model is not necessarily an LLM.
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Quick Recap
Quick glossary of the distinctions that matter most
- AI versus ML: AI is the umbrella; ML learns patterns from data within that umbrella.
- Deep learning: multilayer neural-network methods inside ML.
- Generative AI: systems that produce content; “generative” does not imply consciousness or correctness.
- LLM: a language-focused model, not every generative model.
- Foundation model: broadly pre-trained and adaptable; it may use any modality.
- Prompt engineering: designing runtime input to elicit a desired response.
- Fine-tuning: additional training that changes parameters.
- RAG: retrieval of external context followed by generation; it is not a truth guarantee.
- Hallucination: a plausible-sounding but false, unsupported, or fabricated output.
- Agent: a goal-directed system with an action loop, tools, or environmental interaction.
- AGI: a disputed label for broad general capability, not a default name for an advanced chatbot.
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