A 10-minute primer for your trainees. What Bedrock is, the three-layer stack, the key building blocks (Guardrails, Knowledge Bases, Agents), and how it sits underneath Amazon Quick.
A fully managed AWS service for building and scaling generative AI applications with foundation models — via a single API, without managing any infrastructure.
Bedrock is the developer's entry point to generative AI on AWS. Call any top foundation model — Anthropic Claude, Amazon Nova, Meta Llama, Mistral, Amazon Titan — through one API, add safety Guardrails, ground answers in your own documents with Knowledge Bases, or wire up an Agent that takes multi-step actions. Everything sits inside your AWS security perimeter.
Where Quick is the no-code surface for business users, Bedrock is the API + toolkit for engineering teams building durable AI products. They share the same underlying models.
The apps end users touch. Amazon Quick, Amazon Q, and your own custom applications — chatbots, agents, in-product AI features — all live here.
The service that exposes foundation models to developers, plus the batteries — Guardrails for safety, Knowledge Bases for RAG, Agents for multi-step reasoning, Model Evaluation for choosing the right one.
The pre-trained models themselves — Claude, Nova, Llama, Mistral, Titan Embeddings — pre-integrated into Bedrock, no separate downloads or deployment.
You don't need to teach every Bedrock feature. Four names, one clear job each — that's the level.
InvokeModel or Converse call across every model.A production Bedrock app usually stacks these: Agent orchestrates the reasoning, calling a foundation model via Model Invocation, wrapped in Guardrails for safety, grounded in a Knowledge Base for the domain data. That's four names covering ~80% of real-world Bedrock architectures.
| Feature | What it does |
|---|---|
| Model Evaluation | Compare models on your own tasks with automatic and human judges — pick the right model with data, not vibes. |
| Prompt Management | Version, test, and share prompts as a first-class artefact — not scattered in code. |
| Flows | Visual builder for GenAI workflows — chain a KB retrieval into a Guardrail into a model call. |
| Data Automation | Extract structured data (fields, tables) from unstructured docs and images at scale. |
| Marketplace | Access to third-party specialised models beyond the built-in providers. |
| Provisioned Throughput | Reserve dedicated model capacity for consistent latency at production scale. |
The one comparison your trainees will actually need. Same models underneath, very different surfaces.
| Situation | Tool | Why |
|---|---|---|
| "Give our merchant-support team a policy Q&A assistant" | Quick (Space + agent) | Point at the folder, share the link. Ships this afternoon. |
| "Add AI-powered dispute triage to our contact-centre app" | Bedrock (Agent + KB) | Custom integration inside an existing SLA-bound product. |
| "Prototype an ideation workflow for the founder team" | Quick (chat) | Faster to iterate the prompt than to write code around it. |
| "Ship a mobile-app feature that summarises long PDFs" | Bedrock (Model Invocation + Guardrails) | Native API call from your app, versioned prompts, safety controls. |
| "Explain 'what can AI do for us?' to a non-technical exec" | Quick | You can show it live in 2 minutes. Bedrock needs a code demo. |
| "Fine-grained control over vector-store chunking for 5M docs" | Bedrock Knowledge Bases | Configure indexes, embedding models, retrieval scoring. Quick abstracts this away. |
When trainees ask about the relationship, this is the answer: the chat model behind Quick is a Bedrock foundation model. The Space grounding uses managed RAG concepts identical to Bedrock Knowledge Bases. The chat agents parallel Bedrock Agents. Quick is Amazon's opinionated, no-code surface built on the same rails engineers use for custom builds.
Which means: anything a trainee prototypes in Quick, an engineer can productionise on Bedrock later — same model family, same safety patterns, same architecture. That's a powerful message to leave with the room.
This tab is your natural closer. If you've done the previous four modules right, trainees leave knowing: (1) what Quick does, (2) how to run an ideation demo, (3) where to find free content to keep learning, (4) what Kiro is if they build software, (5) what Bedrock is if they want to go deeper. Five landing pads. That's the whole training day.