Building RAG from scratch means managing ingestion, embeddings, a vector store, and retrieval logic. Knowledge Bases for Amazon Bedrock turns that into declarative configuration.
Retrieval-Augmented Generation (RAG) solves a concrete problem: foundation models don't know your company's internal documents. The fine-tuning alternative is expensive and goes stale every time documents change; RAG instead retrieves relevant fragments at query time and injects them as prompt context.
Knowledge Bases for Amazon Bedrock automates the whole pipeline: it ingests documents from a data source (S3, Confluence, SharePoint, Salesforce, or a web crawler), splits them into chunks, generates vector embeddings with a model like Titan Text Embeddings or Cohere Embed, stores them in a vector store, and exposes a semantic retrieval API. Without this, a team would have to manually orchestrate Textract or a document parser, an embeddings job, and a vector indexing layer.
The most common data source is an S3 bucket with PDF, DOCX, TXT, CSV, or HTML documents. Every time documents are added or modified, a sync job (`StartIngestionJob`) fires that reprocesses only new or changed files -- Bedrock tracks sync status per document, avoiding reprocessing the entire corpus on every update.
Bedrock supports multiple vector store backends: Amazon OpenSearch Serverless (the default managed option, no clusters to provision), Amazon Aurora PostgreSQL with the pgvector extension, Amazon Neptune Analytics, Pinecone, and Redis Enterprise Cloud. The choice matters for cost and latency: OpenSearch Serverless bills per OCU (OpenSearch Compute Unit) with a minimum reserved capacity even at low activity, while Aurora pgvector reuses existing relational infrastructure if you already run databases there, at a lower marginal cost for moderate volumes.
For teams that already have Aurora PostgreSQL in their stack, pgvector is usually the lowest-friction option operationally. For large vector volumes (tens of millions) needing consistently low latency, OpenSearch Serverless scales better with no manual intervention.
Chunking determines how relevant what gets retrieved actually is. Bedrock offers several strategies: fixed-size chunking (fixed token size, with configurable overlap), semantic chunking (splits by semantic boundaries using the embeddings model itself), and hierarchical chunking (small fragments nested inside larger parent fragments, useful when the answer needs broad context but search must be precise).
A frequent mistake is using overly large chunks (2000+ tokens): they dilute semantic relevance and make every query more expensive by injecting more context tokens than needed. Chunks of 300-500 tokens with 10-20% overlap are usually a reasonable starting point for technical documentation and internal policies.
This endpoint does in a single call what used to require three manual steps: searching the vector store, assembling the prompt with retrieved context, and invoking the generator model. The response includes citations with exact references to the fragments used, essential for auditing in regulated sectors.
Knowledge Bases handles static, semantically localizable knowledge well. It doesn't handle well questions that require numeric aggregation over large volumes of structured data (for that, consider a SQL query tool via an Agent), or questions requiring complex multi-hop reasoning over relationships not explicit in the text. Measure recall with a real set of questions from your domain before assuming RAG covers the full use case -- Bedrock Evaluations lets you measure this systematically, a topic we go deeper on in the model comparison article.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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