Amazon Bedrock Multi-Agent Collaboration reached GA at re:Invent 2024, enabling supervisor agents to orchestrate specialised sub-agents across enterprise domains. This is the production reality check: routing quality, token cost multiplication, failure modes that don’t surface until scale, parallel invocation patterns, and the compliance gap that catches regulated industry teams — Guardrails don’t propagate from supervisor to sub-agents.
Read more →Month: January 2025
Edge AI with ONNX Runtime: Running Models On-Device
Last year, I deployed an AI model to a mobile device. The first attempt failed—the model was too large, inference was too slow, and battery drain was unacceptable. After optimizing 15+ models for edge deployment using ONNX Runtime, I’ve learned what works. Here’s the complete guide to running AI models on-device with ONNX Runtime. Figure […]
Read more →Vector Database Comparison: Pinecone vs Weaviate vs Qdrant vs Chroma – Choosing the Right One for Your RAG Application
Last March, a 3AM alert changed everything. Our Pinecone bill had tripled overnight, and I spent the next three months migrating between vector databases, learning hard lessons about what actually matters. Let me share what I discovered—and what I wish someone had told me. Figure 1: Comprehensive comparison of vector database options The Night Everything […]
Read more →Embracing the DevSecOps Landscape in Azure: A Comprehensive Guide
Introduction The world of software development is continuously evolving, and one of the key drivers of this evolution is the need for speed, agility, and security. The DevSecOps approach is gaining traction, as it integrates security practices into the DevOps pipeline, ensuring that applications are developed and deployed in a secure and compliant manner. Microsoft […]
Read more →RAG Optimization: Query Rewriting, Hybrid Search, and Re-ranking
Introduction: Retrieval-Augmented Generation (RAG) grounds LLM responses in factual data, but naive implementations often retrieve irrelevant content or miss important information. Optimizing RAG requires attention to every stage: query understanding, retrieval strategies, re-ranking, and context integration. This guide covers practical optimization techniques: query rewriting and expansion, hybrid search combining dense and sparse retrieval, re-ranking with […]
Read more →