Customizable simulation demo

This is a sample simulation/explainer

This demo can be fully adapted to your own business case, process, dataset, brand, and communication goal. New logic, sliders, visuals, scenarios, metrics, layouts, and custom design elements can be added as needed.

Custom business logic New sliders and controls Tailored visuals Personal branding

interactive AI explainer • assistants • tools • tokens

See How AI Assistants Actually Work

Explore how ChatGPT, Claude, and Gemini turn your request into an answer. Follow the context window, tokens, transformer layers, optional AI tools, and work loops behind complex tasks. This visual AI explainer shows the shared high-level process, not a live internal trace.

step 1 — load context step 2 — route the task step 3 — generate and check result

context language model AI tools answer

language model vs. AI assistant

learned before:language model patterns stored in learned weights

supplied now:context request, chat, files, images, and instructions

used if needed:AI tools web, code, calculator, apps, or file retrieval

what you experience:ChatGPT UI, memory, safety, orchestration, and model output

key product differences

Model version and routing

Context limits and memory behavior

Available tools and file access

Safety rules, interface, speed, and formatting

simple vs. complex AI tasks

simple:usually one main pass — explain, summarize, rewrite, brainstorm, or answer directly from context

complex:plan, use a tool, check, repeat with the new result as context

choose a task type

Simple tasks Complex tasks

what enters this task

complex loop:budget + preferences

Live search

Maps

Draft itinerary

six-step AI workflow

01Load context — build the context window

02Encode input — create tokens and embeddings

03Connect clues — transformer layers

04Choose route — answer, ask, or use a tool

05Do the work — run a multi-step workflow

06Build result — generate one token at a time

in plain English

The answer appears piece by piece. The model scores possible next tokens, selects one, adds it to the growing response, and repeats.

Picture this: very powerful autocomplete that rereads the working context after every new piece.

one level deeper

AI text generation is probabilistic, not database retrieval.

Settings and model behavior shape which likely token is selected. The same prompt can produce different wording.

common myth:there was no complete paragraph hidden inside the model waiting to be copied out

interactive AI explainer how AI assistants work ChatGPT Claude Gemini context window tokens transformer layers AI tools next-token generation

Reality check: This visual AI explainer is an educational high-level model, not a live trace or a claim that the three products have identical internals. Internal reasoning is represented as a task-level work plan, not private chain-of-thought. No prompt leaves this page.

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Preview

Scene 6 of 6

50 / 50 pieces

Read full context again

Score possible next tokens

Use tools if needed

Formatting + safety checks

Generate answer one token at a time

repeating generation loop

Illustrative next-token scores

check 65%

a 16%

however 10%

first 9%

example answer being assembled Use a compact city-center base so most activities stay walkable. Reserve one day for the Old Town and museums, one for Kalamaja and Telliskivi, and one flexible day for Kadriorg.

product-level finishing checks Formatting, task coverage, tool result use, and safety checks may wrap around model generation. These checks are not proof that every factual claim is correct.

watch for The visible answer is assembled sequentially, while product checks may surround it before display.

Society & Education

Interactive AI Explainer: How AI Assistants Actually Work

This is a sample simulation/explainer

See How AI Assistants Actually Work

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