AWS re:Invent 2024 brought significant updates to Amazon Bedrock, and after spending the past month integrating these capabilities into production systems, I want to share what actually matters for enterprise adoption. Having built generative AI applications across multiple cloud platforms over the past two decades, Bedrock represents a meaningful shift in how we can deploy […]
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Azure OpenAI Service with Python: Building Enterprise AI Applications
After spending two decades building enterprise applications, I’ve watched countless “revolutionary” technologies come and go. But Azure OpenAI Service represents something genuinely different—a managed platform that brings the power of GPT-4 and other foundation models into the enterprise with the security, compliance, and operational controls that production systems demand. Here’s what I’ve learned from integrating […]
Read more →The Complete Guide to RAG Architecture: From Fundamentals to Production
Master Retrieval-Augmented Generation (RAG) with this expert-level guide. Learn about RAG types (Naive, Advanced, Modular, Agentic), chunking strategies, embedding models, vector databases, hybrid retrieval, and production best practices with high-quality architecture diagrams.
Read more →Building Production RAG Applications with LangChain: From Document Ingestion to Conversational AI
Introduction: LangChain has emerged as the dominant framework for building production Retrieval-Augmented Generation (RAG) applications, providing abstractions for document loading, text splitting, embedding, vector storage, and retrieval chains. By late 2023, LangChain reached production maturity with improved stability, better documentation, and enterprise-ready features. After deploying LangChain-based RAG systems across multiple organizations, I’ve found that its […]
Read more →LLM Observability: Cost Tracking and Quality Monitoring (Part 2 of 2)
Introduction: You can’t improve what you can’t measure. LLM applications are notoriously difficult to debug—prompts are opaque, responses are non-deterministic, and failures often manifest as subtle quality degradation rather than crashes. Observability gives you visibility into every LLM call: what prompts were sent, what responses came back, how long it took, how much it cost, […]
Read more →AWS Bedrock: Building Enterprise AI Applications with Multi-Model Foundation Models
Introduction: Amazon Bedrock is AWS’s fully managed service for building generative AI applications with foundation models. Launched at AWS re:Invent 2023, Bedrock provides a unified API to access models from Anthropic, Meta, Mistral, Cohere, and Amazon’s own Titan family. What sets Bedrock apart is its deep integration with the AWS ecosystem, including built-in RAG with […]
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