Mastering AWS Knowledge Bases with Bedrock: A Complete Guide

Amazon Bedrock allows you to build scalable GenAI apps using top foundation models like Claude, Titan, and Meta’s Llama. With the addition of AWS Knowledge Bases, you can now inject your internal data into these models through retrieval-augmented generation (RAG).

What is AWS Knowledge Base?

Knowledge Bases allow you to connect structured/unstructured documents to Bedrock’s foundation models. By embedding documents into a vector store like OpenSearch or Pinecone, your LLMs gain context they were never trained on.

Example: Use Knowledge Base to help your chatbot answer support queries with internal PDFs and FAQs.

📘 Learn more about AWS GenAI services here to get familiar with concepts like this.

How Retrieval-Augmented Generation Works

• User asks a question
• Retriever queries your vector store
• Top-K docs are appended to prompt
• Foundation model answers with grounded context

Step-by-Step Setup

1. Upload docs to S3
2. Choose an embedding model (Titan recommended)
3. Configure vector DB (Amazon OpenSearch or Pinecone)
4. Create Knowledge Base in Bedrock console
5. Test and iterate

🔗 Official docs: AWS Bedrock Knowledge Base Guide

Example Use Case: Support Chatbot

Reduce ticket load by grounding a Claude-powered chatbot in your company’s internal documentation.

Embed: User manuals, policy docs, troubleshooting guides.

Learn similar use cases in our AWS certification library.

Service Integrations

• Lambda – trigger vector queries
• API Gateway – expose endpoints
• CloudWatch – log interactions
• S3 – ingest documents

Best Practices

• Chunk documents into 300–500 tokens
• Use cosine similarity
• Test different models for retrieval quality
• Deduplicate or clean irrelevant headers

Simulate these flows using our AI tutor.

Industry Use Cases

• Healthcare: Answer medical policy questions from internal SOPs
• Finance: Ground models in regulatory guidelines
• E-commerce: Help center bots with contextual answers
• EdTech: AI tutor grounded in exam content

Exam Relevance

• AWS AI Practitioner: Domains 3 & 4 test RAG setups
• Solutions Architect Associate: Integration patterns and embedding usage

🚀 Explore all Cloudoku practice exams to prepare.

Conclusion

By leveraging AWS Bedrock with Knowledge Bases, you turn static content into dynamic, intelligent experiences. These tools are shaping the future of cloud-based AI systems.

Check out study tips or our cheatsheets for more guided learning.