Building RAG Applications with Amazon Bedrock Knowledge Bases - Implementing Retrieval-Augmented Generation
Automatically index documents on S3 and unify search and generation with the RetrieveAndGenerate API. Covers chunking strategy selection and safety enforcement with Guardrails.
The RAG Pattern and Knowledge Bases Overview
RAG (Retrieval-Augmented Generation) is a pattern that augments large language model (LLM) responses with external knowledge. An LLM alone cannot answer questions about the latest information or organization-specific data that was not in its training set. RAG searches for documents relevant to the question and passes their content as context to the LLM, producing accurate, evidence-based answers. Bedrock Knowledge Bases provides this RAG pattern as a managed service, integrating document indexing, vector search, and LLM-powered answer generation.
Data Sources and Chunking Strategies
You specify an S3 bucket as the data source, and documents such as PDF, HTML, Markdown, Word, and CSV files are indexed automatically. Documents are split into chunks (fragments), and each chunk is vectorized and stored in the vector store. The chunking strategy is an important design decision that directly affects search accuracy. Fixed-size chunking splits text evenly at a specified number of characters; it is simple, but the context can be cut off. Semantic chunking splits text based on the semantic coherence of sentences and excels at preserving context. Hierarchical chunking uses a two-tier structure of parent chunks (broad context) and child chunks (detailed information); searches match on child chunks, and passing the broad context of the parent chunk to the LLM improves accuracy.
Using the APIs and Guardrails Integration
The RetrieveAndGenerate API accepts a question, performs a relevant document search, and generates an answer in a single API call. The response includes the generated answer along with citation information (S3 URI and relevant passage) for the source documents. The Retrieve API performs search only, returning retrieved chunks for custom processing before passing them to the LLM. Applying Guardrails to Knowledge Bases automatically enforces content filters (blocking inappropriate content), PII masking (automatic removal of personal information), and denied topics (defining topics the model should refuse to answer) during answer generation.
Knowledge Bases Pricing
Knowledge Bases pricing consists of document indexing (vectorization) and query charges. Vectorization is billed at the embedding model rate (approximately USD 0.00002 per 1,000 tokens for Titan Embeddings V2) (as of August 2026). Query charges include the embedding model cost plus the LLM cost for answer generation (approximately USD 0.00025 per 1,000 input tokens for Claude 3 Haiku). When using OpenSearch Serverless as the vector store, OCU (OpenSearch Compute Unit) hourly charges (approximately USD 0.24/hour per OCU) become the primary cost driver. You can choose Pinecone or Aurora PostgreSQL pgvector as alternative vector stores to optimize costs.
Choosing a Vector Store
The search performance and cost of RAG depend on how you choose the store where the vectors are kept. With a managed search platform, there is little build effort and you can get started right away, but you pay according to running time. If you add a vector extension to an existing relational database, you can consolidate operations onto the existing platform, and when the data volume is small to medium, costs are easier to keep down. There is also the option of choosing an external dedicated vector database. Choosing a store that fits your requirements based on data volume, search frequency, and the operational load you can accept is the starting point for building cost-effective RAG.
Improving Search Accuracy
The answer quality of RAG depends on whether the relevant chunks can be retrieved accurately. If you attach metadata to chunks to enable filtering by document type or date, you can prevent unrelated information from getting mixed in. Combining vector search based on semantic closeness with search that looks at keyword matches makes retrieval strong even for exact terms such as proper nouns and model numbers. If you insert re-ranking, which reorders the retrieved candidates by relevance, you can gather the truly relevant content at the top. Raising accuracy at the search stage leads directly to the accuracy of the final answer.
Answer Quality and Using Citations
A major advantage of RAG is that it can present the sources on which an answer is based. By attaching the location and relevant passages of the referenced documents to the generated answer, users can check whether the content is true for themselves. This is an effective countermeasure against hallucination, in which something plausible but incorrect ends up being said. When sufficient evidence cannot be obtained through search, it is also important to design the application to return "I don't know" instead of forcing an answer. By having a mechanism that regularly evaluates the accuracy of answers and finds and improves the patterns with many errors, you can grow it into a RAG application that can be trusted.
Data Updates and Operations
A knowledge base keeps its value only when it is made to follow changes in the source data. When documents in S3 are updated, the index is synchronized to reflect the latest state. If you can ingest only the changes instead of rebuilding everything, you can keep update cost and time down. For documents that contain sensitive information, combine access control that limits which users can view them, so that the content does not leak to people without permission. By defining as operational rules which data is published, when, and to which range of users, you can maintain a knowledge base that is secure and always fresh.
Summary
Bedrock Knowledge Bases is a managed service for implementing the RAG pattern. It automatically indexes documents on S3 and unifies search and generation through the RetrieveAndGenerate API. By optimizing your chunking strategy and applying Guardrails, you can build accurate and safe RAG applications.
References (Official AWS Resources)
The primary sources for this page are the official AWS website and documentation. Check the official pages below for the latest specifications and pricing.
- Amazon Bedrock official page
- Amazon S3 official page
- Amazon OpenSearch Service official page
- AWS Documentation (official)
If this page and the official documentation disagree, treat the official documentation as authoritative.