At the core of RAG, there are always two stages: INGESTION and QUERYING.
Implementation in AWS?
1️⃣ INGESTION: transforming raw data into searchable knowledge
Documents are stored in S3
When new data appears, a Lambda function triggers
It cleans the text, splits it into chunks, and builds embeddings using Bedrock Titan Embeddings
The embeddings are stored in a vector storage, such as OpenSearch Serverless
In the end, we get a knowledge base that can be searched.
An important point: reindexing.
If a single character in a document has changed, there's no point in reprocessing the entire document anew. Smart diffs and incremental updates save both time and money.
2️⃣ QUERYING: searching and generating a response
The user asks a question in the app
The request goes through the API Gateway to Lambda
The question is turned into an embedding and matched against the vector database
The most relevant chunks are passed to an LLM from Bedrock, such as Claude
The finished response is returned to the user
This way, the LLM doesn't respond "from scratch", but relies on real data.
This is the most basic version of RAG on AWS, but the underlying pattern doesn't change when scaling up.
You can add smarter chunking, improved retrieval, caching, orchestration, eval pipelines - the architecture remains the same.
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