palaniappan p 3 min
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 minHow 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 12 minS3 Vectors: 10,000 Results per Query (June 2026)
On June 16, 2026, S3 Vectors raised the QueryVectors limit to 10,000 results per query and cut data-processed charges up to 80% on indexes over 10M vectors. Architecture, pagination, and cost comparison vs OpenSearch and MemoryDB.
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palaniappan p 9 minAmazon 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.