Neural Networks

A practical guide to understanding what neural networks are and why they matter for anyone working with AI.

What neural networks are, how they relate to modern AI, and what product and design teams need to know without needing to understand the mathematics.

22 May 20264 min read

What it is

A neural network is a type of computational glossarySystemA system is a collection of interconnected components that work together to achieve a specific function or outcome.Open glossary term loosely inspired by the structure of the human brain. It is made up of layers of interconnected nodes — sometimes called neurons — that glossaryProcessA process is a defined sequence of steps used to achieve a specific outcome.Open glossary term and transform information as it passes through the network.

Neural networks learn by adjusting the strength of connections between nodes based on the glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term they are trained on. Over millions or billions of training examples, these adjustments result in a glossarySystemA system is a collection of interconnected components that work together to achieve a specific function or outcome.Open glossary term capable of recognising glossaryPatternA reusable solution to a common design problem.Open glossary term, making predictions, and generating outputs that no one explicitly programmed.

Modern AI language glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term — including the ones that power ChatGPT, Claude, and Gemini — are built on a specific type of neural network glossaryArchitectureArchitecture refers to the structure and organisation of a system, including how components interact and are designed.Open glossary term called a transformer. Image recognition, speech processing, and recommendation glossarySystemA system is a collection of interconnected components that work together to achieve a specific function or outcome.Open glossary term also use neural network approaches.

Neural networks are not a recent invention, but advances in computing power and the availability of large glossaryDatasetA dataset is a structured collection of data used for analysis, training models, or processing.Open glossary term have made them dramatically more capable over the past decade.

When to use it

Understand when knowledge of neural networks is practically useful. It is relevant when:

You want to understand the foundations of modern AI capabilities

You are evaluating AI tools and need to understand what lies beneath them

You are working with engineers and want to follow technical conversations

You are explaining AI to stakeholders and need an accurate but accessible account

Key takeaway

Neural networks are the engine behind modern AI. You do not need to build one — but understanding what they are makes you a more informed designer, product manager, or strategist working with AI.

How it works

Understand the basic mechanism. A neural network consists of an input layer that receives glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term, one or more hidden layers that glossaryProcessA process is a defined sequence of steps used to achieve a specific outcome.Open glossary term it, and an output layer that produces the result.

Each connection between nodes has a weight — a number that determines how strongly one node influences another. During training, these weights are adjusted through a glossaryProcessA process is a defined sequence of steps used to achieve a specific outcome.Open glossary term called backpropagation, which updates them based on how wrong the network's output was compared to the correct answer.

After training on enough examples, the network develops the ability to generalise — applying what it has learned to new inputs it has never seen before.

Language glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term are a specific type of neural network trained to predict and generate text. Their scale — billions of parameters — is what gives them the breadth of glossaryCapabilityCapability refers to an organisation’s ability to perform a specific function or deliver a particular outcome.Open glossary term that has made them so impactful.

What this means for designers and product teams. Neural networks are not programmed with rules. They learn glossaryBehaviourBehaviour refers to how users interact with a system, including actions, patterns, and responses.Open glossary term from glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term. This is why they can do things that are difficult to explicitly code — like understanding the nuance of natural language — and also why they sometimes fail in unpredictable or surprising ways.

The glossaryBehaviourBehaviour refers to how users interact with a system, including actions, patterns, and responses.Open glossary term of a neural network is emergent from its training. This means outputs can be hard to fully anticipate, explain, or control in the way that traditional software can be.

What to look for

Focus on:

Emergent behaviour — outputs that were not explicitly designed but arise from training

Failure unpredictability — the ways neural network failures differ from traditional software failures

Training dependency — how performance depends on the quality and scope of training data

Scale effects — how capability changes as models become larger

Interpretability limits — where it is difficult to explain why a model produced a particular output

Where it goes wrong

Most issues come from: Applying traditional software debugging instincts to neural network failures will not work — they fail differently.

Expecting consistent, explainable behaviour from systems that learn from data

Assuming that because a model works well in testing it will work reliably in all production conditions

Underestimating how sensitive performance is to the quality and representativeness of training data

Treating neural network AI as more deterministic and controllable than it is

What you get from it

Understanding neural networks gives you:

A foundational grasp of what modern AI is and how it learns

Better expectations about AI behaviour, failure modes, and limitations

More confident participation in technical conversations about AI products

A more accurate mental model for designing and evaluating AI features

Key takeaway

Neural networks learn from data, not rules. That single insight explains most of what makes AI powerful and most of what makes it unpredictable.

FAQ

Common questions

A few practical answers to the questions that usually come up around this method.

What is a neural network in simple terms?

A neural network is a type of computational glossarySystemA system is a collection of interconnected components that work together to achieve a specific function or outcome.Open glossary term made up of layers of interconnected nodes that learn to recognise glossaryPatternA reusable solution to a common design problem.Open glossary term by processing large amounts of guideTraining DataWhat training data is, how it shapes what an AI model knows and assumes, and what product and design teams need to understand about its role in AI product quality.Open guide. It is the technology underlying most modern AI systems, including language models, image recognition, and speech processing.

Do I need to understand the mathematics to work with AI?

No. Understanding the principles — that neural networks learn from glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term, that their glossaryBehaviourBehaviour refers to how users interact with a system, including actions, patterns, and responses.Open glossary term is emergent and sometimes unpredictable, that glossaryPerformancePerformance refers to how quickly and efficiently a system responds to user actions and processes tasks.Open glossary term depends on training — is what matters for product and design work. The mathematics is for engineers and researchers.

How are neural networks different from traditional software?

Traditional software follows explicit rules that a programmer writes. Neural networks learn their own rules from glossaryDataData is raw information collected and stored for analysis, processing, or decision-making.Open glossary term. This makes them capable of tasks that are too complex to program explicitly, but also means their glossaryBehaviourBehaviour refers to how users interact with a system, including actions, patterns, and responses.Open glossary term can be harder to predict, explain, and debug.

What is a transformer?

A transformer is a specific neural network glossaryArchitectureArchitecture refers to the structure and organisation of a system, including how components interact and are designed.Open glossary term that has become the foundation of modern language glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term. It is particularly good at processing sequences of tokens and understanding glossaryContextThe surrounding conditions that shape behaviour and decisions.Open glossary term across long passages of text. GPT, Claude, Gemini, and most other leading language models are built on the transformer architecture.

Are all AI systems neural networks?

No. Traditional rule-based glossarySystemA system is a collection of interconnected components that work together to achieve a specific function or outcome.Open glossary term, decision trees, and statistical glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term are not neural networks. But most of the AI glossaryCapabilityCapability refers to an organisation’s ability to perform a specific function or deliver a particular outcome.Open glossary term that have attracted widespread attention in recent years — language models, image generators, voice assistants — are built on neural network foundations.

Quick take

You do not need to understand the mathematics, but understanding what neural networks are helps you work more confidently with AI products and teams.

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