· palaniappan p
· 3 min read
Fine-Tuning vs RAG on AWS Bedrock: When to Use Each
Compare fine-tuning and RAG (retrieval-augmented generation) for customizing LLMs on Bedrock. Cost, latency, and accuracy trade-offs.
Tag: rag
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· Palaniappan P · 6 min readHow to Build a RAG Pipeline with Amazon Bedrock Knowledge Bases
Amazon Bedrock Knowledge Bases automate the RAG (Retrieval-Augmented Generation) pipeline — semantic search, chunking, embedding, and context injection into Claude or other foundation models. This guide covers setup, data ingestion, cost optimization, and production patterns.
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· palaniappan p · 10 min readAmazon S3 Vectors: Native Vector Storage Without a Separate Vector Database
Amazon S3 Vectors eliminates the dedicated vector database for many RAG workloads. We compare it to OpenSearch Serverless and MemoryDB and show when each wins.
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· palaniappan p · 9 min readAmazon MemoryDB with Vector Search: Durable Redis-Compatible Storage for AI Workloads
ElastiCache loses your AI chatbot's session memory at every node replacement. MemoryDB doesn't. A decision framework for when to pick MemoryDB over ElastiCache, OpenSearch Serverless, and S3 Vectors for AI workloads — with the latency math and the failure mode that forces the switch.