Generative AI: how it works, where it's heading, and why it matters

What is generative AI?

Generative AI refers to a class of artificial intelligence systems that create new content — text, images, audio, video, or code — rather than simply classifying or analysing existing data. Unlike traditional software, which follows explicit rules written by a programmer, generative models learn statistical patterns from vast training data and use those patterns to produce outputs that are novel yet plausible.

The most prominent family of generative AI today is the large language model (LLM). Models like GPT-4, Claude, Gemini, and Llama are trained on billions of pages of text and learn to predict what word (or, more precisely, what token) should come next in a sequence. That simple objective — next-token prediction — turns out to be extraordinarily powerful, because understanding what comes next requires understanding grammar, facts, reasoning patterns, and even nuance.

On the image side, diffusion models (such as DALL-E, Midjourney, and Stable Diffusion) learn to reverse a noise process: they start with random static and iteratively refine it into a coherent image that matches a text description. The same principle has been extended to video, 3D objects, and music.

The transformer architecture — the engine underneath

Almost every modern generative AI system is built on the transformer, an architecture introduced in the 2017 paper "Attention Is All You Need". The key innovation is the self-attention mechanism, which allows the model to consider every word in the input simultaneously and weigh how much each word matters to every other word. This is radically different from earlier approaches (recurrent neural networks) that processed text one word at a time, left to right.

Self-attention is what gives LLMs their ability to handle long-range dependencies — understanding, for example, that a pronoun at the end of a paragraph refers to a noun introduced several sentences earlier. It's also what makes transformers parallelisable across hardware, enabling training on enormous datasets using thousands of GPUs.

The transformer architecture comes in three flavours:

• Encoder-only — reads the full input and produces a representation of it. Used for classification and search (e.g. BERT).
• Decoder-only — generates text token by token, conditioned on what has come before. This is the architecture behind GPT and most modern chat models.
• Encoder-decoder — reads the full input, then generates an output sequence. Used for translation and summarisation (e.g. T5).

How these models are trained

Training a large language model happens in stages, each with a different objective:

• Pre-training — The model reads vast amounts of text from the internet, books, and code repositories, learning to predict the next token. This stage requires enormous compute (thousands of GPUs for weeks) and produces a model with broad knowledge but no particular alignment to human preferences.
• Supervised fine-tuning (SFT) — Human annotators write example conversations showing the kind of responses the model should give. The model is trained on these examples to improve helpfulness and reduce harmful outputs.
• Reinforcement learning from human feedback (RLHF) — Humans rank multiple model outputs for the same prompt. A reward model is trained on these preferences, and the language model is further optimised using reinforcement learning to produce outputs that align with human judgments of quality.

The result is a model that not only knows a great deal but can communicate that knowledge in a helpful, conversational way. The entire pipeline — pre-training, SFT, RLHF — has become the standard recipe for building production-grade LLMs.

Key milestones in generative AI

The rise of autonomous agents

The most significant evolution in generative AI right now is the shift from reactive chat to proactive agents. A chat model answers when asked. An agent, by contrast, can take a goal, break it into sub-tasks, decide which tools to use, execute steps, evaluate results, and iterate — all with minimal human intervention.

The building blocks of an autonomous agent are:

• Planning — The agent decomposes a high-level goal into an ordered sequence of steps, identifying dependencies and decision points.
• Tool use — The agent can call external tools: search engines, code interpreters, APIs, databases, file systems, or even other AI models.
• Memory — Short-term (conversation context) and long-term (stored knowledge, past interactions) memory allow the agent to maintain state across sessions.
• Self-reflection — The agent evaluates its own outputs, detects errors, and adjusts its approach. If a code snippet fails, it reads the error message and tries again.

Frameworks like LangChain, AutoGen, and CrewAI make it possible to build multi-agent systems where specialised agents collaborate — one researches, another writes, a third reviews — much like a human team. This is still an emerging capability, but the pace of improvement is rapid, and production-grade agent systems are already being deployed in software development, customer support, and data analysis.

What this means for businesses

Generative AI is already transforming how organisations operate, and the shift is accelerating. Practical applications today include:

• Content creation — Drafting marketing copy, reports, documentation, and internal communications at speed, with human review and refinement.
• Code generation — AI-assisted development that writes boilerplate, suggests implementations, reviews pull requests, and generates tests.
• Customer interaction — Intelligent chatbots and copilots that handle routine queries, surface relevant knowledge-base articles, and escalate complex cases.
• Data analysis — Natural-language interfaces to databases and dashboards, allowing non-technical users to ask questions of their data and get instant answers.
• Process automation — Agentic systems that handle multi-step workflows — invoice processing, compliance checks, onboarding sequences — with human oversight at decision points.

The organisations that will gain the most are those that treat generative AI not as a novelty but as infrastructure — embedding it into workflows, training teams to use it effectively, and building feedback loops that let models improve over time.