AI and Machine Learning Fundamentals
This is a guide on AI and machine learning fundamentals.
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How core AI terms relate
AI, machine learning, deep learning, neural networks, and large language models are related, but they are not interchangeable.
This module gives you a simple hierarchy you can reuse when customers or internal teams use “AI” loosely. This isn’t a memory test. The goal is to understand the relationship between the terms well enough to clarify what someone means.
AI, ML, deep learning, and neural network hierarchy
AI refers to machines or software performing tasks that appear intelligent—things like understanding language, recognizing patterns, making predictions, planning, or generating content.
Machine learning refers to systems that learn patterns from data instead of relying only on fixed rules. For example, a machine learning system could learn from historical examples to identify patterns or make predictions.
Deep learning uses neural networks with many layers to learn complex patterns from large amounts of data. It’s important because many modern AI capabilities rely on it, including language, image, audio, and multimodal systems.
They are commonly used in deep learning and help systems learn patterns from data. You do not need to understand the technical mechanics of neural networks in this course. For now, just know that neural networks are a structure used by many deep learning systems.
A simple way to remember the hierarchy is:
AI is the broad category. Machine learning sits inside AI. Deep learning sits inside machine learning. Neural networks are the structure deep learning commonly uses.
Why the hierarchy matters in partner conversations
Customers can use the word “AI” to mean many different things.
That could mean several things. They might be referring to fixed business rules, a machine learning classifier, a generative AI workflow, or a combination of different systems.
At this stage, don’t correct the customer or prove you know the exact terminology. Your goal is to clarify what they mean so the next conversation is more useful.
That kind of question keeps the conversation grounded. It also helps you avoid assuming that every AI use case involves the same technology or implementation path.
Where large language models fit
Large language models, often called LLMs, are one important type of AI model.
An LLM is a type of deep learning model trained to process and generate language.
LLMs are behind many familiar generative AI experiences, including chat, drafting, summarization, reasoning support, and content generation.
Some modern LLMs are multimodal, meaning they can work with more than one type of input—such as text, images, or audio—depending on the model and product.
You’ll hear about LLMs often because many modern AI experiences use language as the way people interact with intelligence at work.
Users might ask a question, request a summary, draft a response, or analyze information.
But LLMs are not the whole of AI.
Many AI systems do not center on language generation
Some systems classify transactions, detect patterns, recommend items, analyze images, process audio, or support decision workflows. Some systems combine multiple AI capabilities.
Several common AI terms are connected, but they do not mean the same thing. AI is the broadest category. Machine learning is a subset of AI. Deep learning is a subset of machine learning. Neural networks are structures commonly used in deep learning.
LLMs are an important type of AI model, especially for language-based work. Some modern LLMs are also multimodal and can work with inputs such as text, images, or audio, depending on the model and product. LLMs are still not the whole field of AI.
The most important partner habit is clarification. When someone says “AI,” listen for what they actually mean before assuming which technology or solution path is involved.
What “a model” means
“Model” is one of the most important words you will hear in AI conversations.
It is also one of the easiest words to misunderstand because people use it in more than one way.
A customer might use “model” to mean an AI system generally. A technical team may use “model” to mean a specific named option selected in a product or API.
One word, two common meanings
Generally, a model is a trained system that maps inputs to outputs based on patterns it has learned.
For example, a model might receive a piece of text and generate a summary.
It might receive an image and identify what is in it. It might receive structured data and classify a record.
In a more specific product or platform conversation, “model” can also mean a named model option used in a product, interface, or API configuration.
Be mindful that two people may use the same word differently.
Inputs, outputs, and context
At a high level, models receive inputs, process them, and produce outputs. Inputs are what the model receives.
Inputs may include text, images, audio, structured data, instructions, examples, uploaded files, or information made available through an approved workflow or connection.
Outputs are what the model produces. Outputs may include text, summaries, classifications, recommendations, images, audio, or tool-use requests that a connected product or workflow may turn into actions within the right boundaries.
Context is the information the model uses to shape its response.
Context may include:
The user’s instructions
Documents or examples
Conversation history
Connected data
Workflow information
Organizational knowledge
Policies
Permissions
Details from connected systems
This affects business conversations. A model does not automatically know a customer’s private documents, internal policies, product catalog, or workflow rules. That information must be provided through the right product setup, approved connection, retrieval method, or workflow.
This is one reason workflow context matters. Before discussing a product or model, clarify what information the AI needs, where that information comes from, and who should be allowed to use it.
That wording can overstate what the model has access to and create confusion about data, permissions, and governance.
Model versus product, platform, or API surface
Model
The trained system that processes inputs and generates outputs.
Product or workspace
The user-facing experience where people interact with AI.
API surface
The developer-facing interface used to build AI into products or workflows.
ChatGPT is an example of a product experience where users can interact with AI through natural language and supported tools.
The same product or API surface may support different models.
The same model may also be available through different experiences, depending on product, plan, configuration, or availability.
You do not need to understand API configuration, endpoint selection, or implementation details. The key is to avoid confusing the model with the experience or interface where the model is used.
This distinction also helps you understand where different OpenAI capabilities may fit.
ChatGPT, Codex, and applications built with the API may expose or use models differently. Agent capabilities and the decision to deploy open-weight models are separate considerations that depend on the workflow and operating environment.
“Model” means two related but different things.
In a general AI conversation, a model is a trained system that maps inputs to outputs. In a product or platform conversation, “model” may refer to a specific named option.
It is also important to separate the model from the product or API surface where it is used. This helps keep customer questions clear and reduces assumptions about access, availability, or implementation.
Model vocabulary partners may hear
You may hear model vocabulary that sounds technical: transformer, foundation model, and frontier model.
You only need recognition-level understanding. These terms describe different dimensions of a model. They are not synonyms, and they should not be used interchangeably.
Transformer
A transformer is a way of structuring a model. It is not a product. It is not a specific named model. It describes something about how a model is built.
Transformers are important because they helped enable major progress in language, code, and multimodal AI systems.
Many modern AI systems use transformer-based approaches or ideas that build on them.
“Transformer” tells you something about model architecture. It does not tell you whether the model is the right fit for a customer use case.
Foundation model
A foundation model is a general-purpose AI model that adapts to support many different tasks.
This term describes breadth of use.
A foundation model supports tasks such as generating text, summarizing information, analyzing data, answering questions, supporting coding workflows, or working with other capabilities depending on the product or system it is part of.
Foundation models are significant because they can often be adapted or directed for different business workflows. They may be used through prompting, tools, retrieval, fine-tuning, or product-specific workflows.
But “foundation model” does not automatically mean OpenAI. It also does not automatically mean the model is the best fit for every customer use case.
A simple way to frame this is:
“Foundation” points to breadth of use. It does not make the model a customer-ready solution by itself.
Frontier model
A frontier model is a point-in-time label for a model considered among the most capable available when the comparison is made.
This term describes current capability level.
Customers ask about frontier models when they want the latest capability, stronger reasoning, advanced performance, or support for more complex work.
The important point is that “frontier” is a moving label. Which models are considered frontier can change as new models are released and capabilities improve.
That makes verification important.
Before naming a specific frontier model in a customer-facing commitment, use current official sources.
Keeping the terms separate
Transformer
How the model is built.
Foundation model
How broadly the model can be used or adapted.
Frontier model
How advanced or capable the model is at a given time.
They are not interchangeable.
A model could be described by more than one of these terms, but each term answers a different question.
When a customer uses one of these terms, clarify which dimension they are asking about:
Are they asking about architecture? Are they asking about breadth of use? Are they asking about current capability? Are they asking which model option should be used in a specific product or technical setup?
That clarification keeps the conversation accurate without requiring you to go into implementation detail.
The main takeaway is that these three terms—transformer, foundation model and frontier model—answer different questions.
When a customer uses one of them, clarify what they are asking about before moving toward a product or model recommendation.
Describe capability first, verify names when needed
A lot of things change over time: model names, model families, recommended choices, pricing, availability, and product support to name a few.
That creates a communication risk. If you lead with a specific model name too early, your language may become brittle or inaccurate. If you describe the needed capability first, your explanation stays more durable and more connected to the customer’s actual problem.
This module introduces a practical habit: Describe capability first. Verify exact model names when they matter.
Why this habit matters
Customers usually care less about the name of a model than about what the AI system needs to do.
They may need a model that can summarize a large volume of customer feedback. They may need stronger reasoning for multi-step analysis.
They may need a cost-optimized option for a high-volume workflow. They may need multimodal capability, such as working with both text and images.
Those are capability needs.
Capability-first language helps you keep the conversation focused on the work being supported, not just the technology label.
It also leaves room for technical validation when exact choices matter.
A useful partner standard is:
Be specific when specificity is required. Stay capability-led when the exact name is not the point.
When exact model names matter
Sometimes exact model names do matter
When a developer is implementing or configuring a system. When pricing, cost, throughput, latency, or availability is being discussed. When a partner is making a specific commitment to a customer. In technical documentation that must remain accurate over time.
Use exact model names when they are required for:
Code or configuration
Pricing or commercial discussion
Availability or plan guidance
Customer commitments
Implementation documentation
Formal solution recommendations
When exact names matter, verify them against current official sources before committing.
A helpful way to think about it is:
Capability-first language is good for discovery and early explanation. Exact model names require verification before commitment.
Remember this communication habit: describe capability first, then verify exact model names when needed.
Capability-first language keeps the conversation focused on what the customer needs the AI system to do. Exact model names still matter for implementation, pricing, availability, and formal commitments.
The key is knowing when to stay capability-led and when to verify a specific model option before giving guidance.
How Systems Learn
You may hear people describe different ways AI systems learn.
This module introduces three common terms at recognition depth only:
Supervised learning
Unsupervised learning
Reinforcement learning
You do not need to design a training pipeline or explain detailed learning mechanics. You just need to recognize the basic difference between these terms when they appear in customer, partner, or technical conversations.
Supervised learning
Supervised learning uses examples with known answers.
The system learns from input-output pairs. That means it sees examples where the correct label, category, or value is already known.
A simple analogy is a learner practicing with an answer key. The learner sees the question, checks the correct answer, and gradually learns the pattern.
For example, a supervised learning system might learn from historical records where each example includes both the input and the known outcome.
Over time, the system learns patterns that help it classify or predict new examples.
Supervised learning is useful when examples and expected outputs can be clearly defined.
Unsupervised learning
Unsupervised learning looks for patterns in data without a provided answer key.
At a high level, the system groups or clusters, identifies structure in unlabeled data.
The system is not being shown the correct answer for each example. Instead, it is looking for patterns that may be useful.
For example, an unsupervised learning system might group similar items together based on patterns in the data. The value is in discovering structure, not predicting a known label.
Think of it like exploring a city without a map and gradually noticing neighborhoods or routes.
Unsupervised learning is useful when the goal is to discover structure or patterns, not predict a known answer from labeled examples.
Reinforcement learning
Reinforcement learning improves through feedback over a sequence of actions.
The system learns which actions lead to better outcomes over time. This is different from learning from a static set of labeled examples.
The system is improving behavior based on feedback from actions and outcomes—like learning through trial and adjustment.
Reinforcement learning is often associated with control problems, sequential decision-making, and systems where actions affect future states.
Reinforcement learning is about improving behavior through feedback over time, not simply learning from a fixed dataset.
Recognizing mixed systems
Real AI systems may combine multiple approaches. A system may use one approach during development, another approach for refinement, and additional processes for evaluation, safety, or product behavior.
In customer conversations, you do not need to force every system into one pure category.
Instead, focus on the main learning idea being discussed.
Is the system learning from labeled examples, finding patterns without labels, or improving through feedback over time?
That question helps you recognize the difference without overreaching into technical design. It also reminds you that real customer solutions may combine several techniques or portfolio components, so the right next step is often clarification rather than a quick label.
The main takeaway is that “learning” can mean different things in AI. Some systems learn from examples where the expected answer is known. Others look for patterns in data without an answer key. Some improve through feedback over time.
In partner conversations, you do not need to classify every system perfectly. What matters is recognizing the kind of learning being discussed and asking the right follow-up questions when clarification is needed.