Is the AWS Bedrock Academy free?

Yes. The full course is free and includes 25 modules, 52 lessons, 16 labs, and quiz-based progression with no sign-up required.

How is Infinity AWS Bedrock Academy different from AWS Skill Builder?

Unlike AWS Skill Builder which often gates advanced hands-on labs behind paid subscriptions or AWS Builder ID logins, Infinity Tech Stack's AWS Bedrock Academy is 100% free, ungated, and browser-first with zero sign-up required. It includes 25 modules, 52 lessons, 16 hands-on labs, and 125+ quiz checks covering the Converse API, Bedrock Guardrails, AgentCore, and multi-model evaluations without requiring active AWS account credentials.

Who is this course for?

Developers, architects, and platform teams building AI systems on AWS Bedrock from MVP to production scale.

What does this Bedrock course cover?

The academy covers runtime inference, prompt engineering, guardrails, knowledge bases, agents, evaluation, observability, security, compliance, resilience, incident response, and production launch discipline.

When should you choose a Customer-managed Knowledge Base over Managed?

When you need full control of the vector store and ingestion pipeline — Customer-managed knowledge bases give full control of vector store choice and ingestion, at the cost of managing that infrastructure yourself.

Which output should RAG systems prefer in enterprise apps?

Cited answers with source references — Citations improve auditability and user trust.

RAG Architecture in Bedrock Tutorial | Knowledge Bases and RAG

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1. RAG Architecture in Bedrock2. Chunking, Ranking, and Freshness

RAG Architecture in Bedrock

📚 Knowledge Bases and RAG⏱ 12 min⭐ 80 XP⌨ Hands-on lab

Ground Responses in Enterprise Data

RAG combines retrieval and generation so model answers are grounded in your approved documentation. Amazon Bedrock Knowledge Bases offers two paths:

Managed Knowledge Base (recommended) - AWS handles ingestion, chunking, embedding, re-ranking, and storage auto-scaling. Built-in connectors cover Amazon S3, SharePoint, Confluence, Google Drive, OneDrive, and Web Crawler, with document-level ACL filtering at query time and Smart Parsing for PDFs, PPTX, DOCX, and embedded visuals.
Customer-managed Knowledge Base - you own the vector store (OpenSearch Serverless, Aurora, Neptune) and the full ingestion/indexing pipeline for maximum control.

Ingest source documents (or connect a structured/graph data source).
Create embeddings for semantic retrieval (multimodal embeddings for image-aware search).
Fetch top relevant chunks at query time, optionally via agentic multi-hop retrieval.
Compose a grounded prompt with citations.

Managed Knowledge Bases integrate natively with AgentCore Gateway, so any MCP-compatible agent framework can discover and call your knowledge base as a tool with no custom glue code. RAG reduces hallucination risk and improves trust when citations are surfaced.

⌨ HANDS-ON LABDesign a Retrieval Pipeline

⭐ +150 XP

Map documents, embeddings, retrieval, and response synthesis for a support assistant.

1Define your corpus folders for ingestion.

2Write a retrieval acceptance checklist.

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$

OBJECTIVE 1 / 2 — type "hint" if stuck

🧪 Knowledge Check

Press 1-4 to select1 of 3

What is the biggest reliability gain from RAG?

Infinite context size

Grounded responses from approved corpora

No need for prompts

No need for evaluation