The no-code build surface for generative AI. Everything a PINd.AI trainer needs to explain what Quick is, what it does, and where it fits — before running the ideation demo in Module 02.
A generative-AI-powered platform that lets business teams chat, research, build apps, and stand up grounded agents — in plain language, anchored to their own documents. No ML expertise required.
Amazon Quick is AWS's no-code AI workspace — chat with a foundation model, run deep research across your files, build small web apps by describing them, and turn a folder of documents into a grounded assistant. It's the fastest path from "we have a problem" to "we have a working prototype".
Under the hood it uses the same foundation-model ideas as Amazon Bedrock — but the surface is designed for business users, not developers. If Bedrock is the engine, Quick is the dashboard.
Trainees will land here first. It looks like any AI chat, but with two big differences: it can read documents you drop into the conversation, and it can output rich, structured content — Markdown tables, HTML pages, downloadable files — not just text bubbles.
Trainer note: this is the surface we use for the Module 02 ideation demo. Everything happens in chat.
The two AI-first surfaces every user should meet in the first ten minutes.
You'd expect a chat box. What surprises people is that you can drop a PDF, a slide deck, or a CSV straight into the conversation, and Quick will read it. Ask "summarise this in 5 bullets", "turn this policy into an FAQ", or "give me an HTML proposal from these notes" — and the output is rich, formatted, and downloadable.
For the ideation demo in Module 02, we lean on exactly this: drop the workshop notes into chat, ask for a structured proposal, refine, then export.
Research is the step up when a question needs a real answer, not a one-liner. It plans the topics to cover, searches your Space and the web, cross-references sources, and hands back a cited report in 2–5 minutes.
Every claim links back to where it came from — so a compliance or risk reviewer can verify before acting. It's a first draft, not the final call.
Describe what you want to investigate, in detail. The more specific you are — which data to analyse, which questions to answer, what format you want back — the sharper the output.
Example: "Compare three AI-first startup ideas for the fintech space. Score each for market size, feasibility, and 12-month cost-to-launch. Output as an HTML brief."
Chat is where you start; Research is where you go when the room asks a hard question mid-session. Show both — most trainees remember Research most vividly because "the AI kept working for 3 minutes before answering".
Where Quick stops being "a smarter chat" and starts being "a tiny app studio".
Tell Quick what the app should do, who uses it, and what data it needs. It generates a responsive web app in real time — forms, tables, charts, navigation, built-in AI inference — while you watch. You refine it by conversation, then publish with one click and share by URL.
Great examples to hand your trainees: a pilot-idea tracker for the team, an FAQ builder from a policy PDF, a lightweight review form for merchant onboarding.
Plain language: "Build a tracker for our 20 candidate startup ideas, showing score, owner, and status. Add a filter by category." The more detail, the closer the first build lands.
In a few minutes Quick builds a working app — responsive UI, data connections, interactive elements. Grant it access to the Space and to AI inference when prompted.
A live preview runs in your browser exactly as your team will see it. Click through, submit a form, check the data flow.
Refine by conversation: "Add a column for scoring" or "Sort by descending score". Each prompt refines the existing app — no restart.
One-click publish makes it live at a URL for anyone with permission. Version history lets you roll back.
A Space is a collection of files, dashboards, and knowledge bases scoped to a team or project — think of it as a shared, AI-searchable folder. When you ask Quick a question inside a Space, it only searches data relevant to that context. One person organises it once; the whole team benefits.
Point a chat agent at a Space and you have a domain-specific assistant that answers only from that content, with citations. It stays on-policy and traceable — because it can only quote what's actually in the folder.
Three layers of AWS AI. Trainees usually meet the top; the value comes from understanding what's underneath.
Business users chat, research, build apps, and stand up grounded agents in plain language. Nothing to install, nothing to configure — a browser and an AWS account are enough.
Managed service for developers to call foundation models via API, add Guardrails, wire up Knowledge Bases for RAG, and orchestrate agents. This is where custom applications live — including the models Quick itself uses under the hood.
Anthropic Claude, Amazon Nova, Meta Llama, Mistral, Amazon Titan Embeddings — the pre-trained models that do the reasoning, generation, and vector encoding. Bedrock exposes them; Quick uses them.
The models behind Quick's chat are the same class of models a developer calls through the Bedrock API. The capability — reading a document, answering with citations, generating a table — comes from the model. The experience — no code, drag-and-drop files, "publish to URL" — comes from the surface. Explain it that way and the room understands why the two products exist side by side.
| Situation | Reach for… | Why |
|---|---|---|
| Business team wants to prototype a workflow this afternoon | Quick | Fastest path from idea to prototype. Zero setup. |
| Building a customer-facing product with strict SLAs | Bedrock | Full control over model, prompts, guardrails, monitoring. |
| Internal Q&A grounded in a folder of policies | Quick (Space + chat agent) | Point at the folder; done. No RAG plumbing. |
| High-volume production RAG across millions of docs | Bedrock Knowledge Bases | Fine-grained control over chunking, vector store, retrieval scoring. |
| Explain "what could AI do for us?" to a non-technical exec | Quick | You can show it working in 2 minutes. Bedrock takes a code demo. |
Ten-minute overview: foundation models, Guardrails, Agents, Knowledge Bases, and the three-layer stack.
How to teach this module in 20 minutes. Copy the pattern, adapt the examples.
Open with the one-line answer, then a single provocative question. Something like: "If we could turn every folder in the company into an assistant that answers with citations, what changes about how we work?" — let the room sit with it for 10 seconds before you demo.
Don't teach the four capabilities as a feature list. Pick one story — a startup idea → market research → HTML proposal → shareable app — and hit all four capabilities in sequence. Trainees remember the flow, not the checklist.
Order that works: Chat → Research → Apps → Spaces & Agents. Chat is the friendliest entry; Research earns the "wow"; Apps shows durable value; Spaces closes the loop with grounding.
Show the three-layer diagram from Tab 4. Land three points:
End with: "That was the tour. Now let's actually build something in the next 30 minutes." Then click through to Module 02 and start the ideation demo live. Momentum matters.
| Question | Clean two-line answer |
|---|---|
| Is my data used to train the model? | No. Your uploaded content isn't used to train the underlying foundation models. It's used to ground your session and (in a Space) your team's grounded assistants. |
| How does Quick compare to ChatGPT? | Similar chat feel, different foundation. Quick sits inside AWS's security model, connects to your Spaces natively, and can generate apps — not just text. |
| Do I need to know Bedrock to use Quick? | No. Quick is designed for zero-code use. Bedrock only comes in when a developer wants to build a custom product on the same underlying models. |
| Can it hallucinate? | Yes — like any LLM. That's why we ground answers in Spaces with citations, and why a human should sign off on anything customer-facing. |
| Is it free? | The chat + Research + Apps + Spaces core is on a free/plus tier at quick.aws.com. Enterprise features (SSO, admin, API, QuickSight) run through an AWS account. |
If the room is quiet, jump to the ideation lab early — a live build always re-engages people. If the room is chatty, stretch this module by asking each participant for one problem they'd point Quick at.
Step-by-step: from a fuzzy startup idea to a shareable HTML proposal in ten Quick prompts.