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Resource

How LLMs actually work.
Enough to debug them.

You do not need the mathematics. You do need a model of what is happening, because almost every surprising behaviour becomes predictable once you have one — and stays baffling without it.

All Resources →
Pixel illustration for How LLMs Actually Work: a loom weaving coloured threads between connected systems.

The mechanism

  • Text becomes tokens.

    Not words and not characters — fragments, chosen by a compression scheme fitted to a training corpus. Common words are single tokens; unusual ones split into several. This is why models are bad at counting letters in a word: they never see the letters, only the fragments.
  • The model predicts the next token.

    That is the entire mechanism. Given everything so far, it produces a probability distribution over what comes next. Everything a model appears to do — reasoning, summarising, coding, refusing — is that operation repeated, with the output appended and the process run again.
  • Attention decides what matters.

    When predicting the next token, the model weighs every previous token by relevance. This is what lets it connect a pronoun to a noun forty sentences back, and it is also why cost scales the way it does with context length.
  • Sampling introduces variation.

    The model gives a distribution; something has to pick. Picking the highest-probability token every time gives repetitive text, so a sampling strategy adds controlled randomness. That is why the same prompt gives different answers, and why lowering temperature makes output more predictable rather than more correct.
  • Training happened once, before you.

    Pre-training on a large corpus, then fine-tuning toward helpfulness and safety. The weights are frozen when you use it. It has no memory between conversations, no ability to learn from you, and no access to anything after its training cut-off unless you put it in the context.

Why this explains the failures

01

Why it invents things

It produces the most plausible continuation, and a plausible continuation of “the case citation is” is a well-formed citation. It has no mechanism for distinguishing recalled from constructed. Retrieval fixes this by putting the real answer in the context; nothing about the model itself does.
02

Why it cannot count characters

It sees tokens, not letters. Asking how many times a letter appears in a word is asking about information the model never received. This is not a capability gap that scale fixes.
03

Why prompt wording matters so much

The prompt is the entire conditioning. Changing phrasing moves the distribution over continuations, sometimes substantially. This is why prompts need testing rather than reasoning about, and why a change that seems cosmetic can move quality.
04

Why it forgets

There is no memory. Every conversation is the whole history re-sent as context. When it exceeds the window, something is dropped — and unless your system is deliberate about what, it will be dropped arbitrarily.
Questions

Questions about How LLMs Actually Work.

Does it understand what it is saying?

A question about meaning that we are not equipped to settle. What is answerable is the engineering version: it produces outputs consistent with understanding across a wide range of tasks, and it fails in ways a person who understood would not. Design for the second observation and you will build systems that hold up.

Does it learn from our conversations?

Not within a conversation and not between them — the weights are frozen. Some providers may use submitted data for future training depending on your agreement, which is a contractual question rather than a mechanical one, and one worth checking rather than assuming.

Why does the same prompt give different answers?

Sampling. The model gives probabilities and something picks from them, with deliberate randomness. You can reduce it with temperature settings, and reducing it makes output more consistent rather than more accurate — a distinction worth keeping straight.

The failures stop
being surprising.

Most AI project problems we are called into are explained by one of the five mechanisms above.

30-minute technical call · no deck

Shipped & in build

Real work. Check it yourself.

What we built, what stage it is at, and a link to the live thing wherever there is one.

The updated yourOS homepage: Roxy’s open ring surrounded by bento cards for calls, voice, translation, building, design, apps and more.
The yourOS homepage on a phone, with its compact navigation, welcome film button and bento cards.
youros.tech — the homepage bento hero
Our own · coming soon

yourOS

yourOS is an operating system with an AI at its heart. You talk to it like a person, out loud or typed. It makes your calls, plans your days and works on your projects with a crew of agents, on your own private server. Our own product, built from the first commit; the waitlist is open.

What we built
  • Operating system
  • Roxy
  • Agent crew
  • Voice calls
  • App store
  • Website
Meet yourOS →
The Sonora homepage on desktop: a full-bleed dune landscape behind the headline “Transform Your Life with Sound”, with App Store and Google Play download buttons.
The Sonora homepage on a mobile viewport, showing the same hero reflowed to a single column.
sonora.com — homepage, 1440×900
Live · iOS · Android

Sonora

Website, backend and app assets for Sonora, a free AI sound-therapy app on iOS and Android published by Cognitive AI.

What we built
  • Website
  • Backend
  • App assets
Read the case study →
The updated Podswap homepage on desktop: “Grow on Instagram and TikTok for free, with real engagement from creators your size”, beside a tilted wall of exchange, tool and guide cards on a violet gradient.
The updated Podswap homepage on a phone, with its floating navigation, Instagram and TikTok engagement headline and tilted tool cards.
podswap.app — homepage, 1440×900
Five platforms

Podswap

Brand, website, prototype, apps on five platforms, the AI backend and the algorithm-intelligence pipeline for Podswap, a social growth engine that runs without a human in the loop.

What we built
  • Brand
  • Website
  • Prototype
  • Apps on five platforms
  • AI backend
  • Algorithm pipeline
Read the case study →
The unOpen.AI homepage introducing Roxy, a private AI assistant that remembers who you are.
The unOpen.AI homepage on a mobile viewport.
unopen.ai — homepage, 1440×900
Prototype · closed beta

unOpen.AI

In closed beta: Roxy, a private AI assistant with permanent memory running on infrastructure the user owns. Brand, site, product, backend and conversational design.

What we built
  • Brand
  • Site
  • Product
  • Backend
  • Conversational design
Read the case study →
coherence.com on desktop, headed "Intelligence that moves in phase with people".
coherence.com on a mobile viewport, reflowed to a single column.
coherence.com — homepage, 1440×900
Live

Coherence

Live at coherence.com: brand identity generated with AI agents we directed, the motion system and website for the group behind Sonora, AboutAI, WakeUp and MudShadows. Agent work remains in build.

What we built
  • Brand identity
  • Motion system
  • Live website
  • Agent work — in build
Read the case study →
The new Domain Booth homepage on desktop: the headline “Premium Domains. Serious Buyers. Proven Results.” beside the animated DB monogram.
The new Domain Booth homepage on a phone, reflowed to a single column.
Our build of the Domain Booth homepage — 1440×900
Live

Domain Booth

Live at domainbooth.com for DomainBooth, a premium domain brokerage: a new animated mark, every page redesigned and rebuilt with designed mobile layouts, and a custom CMS for its sales record, featured names, press and enquiries.

What we built
  • Animated mark
  • Every page redesigned
  • Designed mobile layouts
  • Custom CMS
Read the case study →
Who we have built for

Twenty-one years. Names you know.

Apps, platforms and campaigns for these teams, long before the AI work. Every one is written up on the work page — what we did, and where our part ended.

See all of our work →
  • BBC Good Food logo
  • Charities Aid Foundation logo
  • GAME logo
  • NatWest logo
  • Next logo
  • NSPCC logo
  • Tesco logo
  • Worldpay logo
  • Samsung logo
  • Sony logo
  • Land Rover logo
  • UAP Limited logo
  • Netflix logo
  • Amazon Prime Video logo
  • Mercedes-Benz dealerships logo
  • San Miguel logo
  • Biovene Barcelona logo
  • Seventy7 logo
  • Littlewoods logo
  • Smile logo
  • The Co-operative Bank logo