A Beginning-of-Year Reflection for Enterprise Architects and Technical Leaders As we step into 2026, it’s worth pausing to reflect on the seismic shifts that defined enterprise architecture in 2025—and the hard lessons learned when AI hype met production reality. What began as breathless excitement around generative AI and LLMs has matured into a more nuanced […]
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From RAG to Agents: The Evolution of AI Applications in 2025
From RAG to Agents: The Evolution of AI Applications in 2025 A Comprehensive Analysis of How AI Applications Evolved from Retrieval-Augmented Generation to Autonomous Agent Systems December 2025 | Industry Whitepaper Retrieval-Augmented Generation (RAG) revolutionized how we build LLM applications by grounding responses in real data. But RAG has limitations: it’s reactive, constrained to retrieval […]
Read more →Mastering LangChain: The Complete Getting Started Guide to Building Production LLM Applications
Introduction: LangChain has emerged as the de facto standard framework for building applications powered by large language models. Originally released in October 2022, it has grown from a simple prompt chaining library into a comprehensive ecosystem that includes LangChain Core, LangChain Community, LangGraph, and LangSmith. With over 90,000 GitHub stars and adoption by thousands of […]
Read more →ETL for Vector Embeddings: Preparing Data for RAG
Preparing data for RAG requires specialized ETL pipelines. After building pipelines for 50+ RAG systems, I’ve learned what works. Here’s the complete guide to ETL for vector embeddings.
Read more →Production RAG Architecture: Building Scalable Vector Search Systems
Three months into production, our RAG system started failing at 2AM. Not gracefully—complete outages. The problem wasn’t the models or the embeddings. It was the architecture. After rebuilding it twice, here’s what I learned about building RAG systems that actually work in production. Figure 1: Production RAG Architecture Overview The Night Everything Broke It was […]
Read more →Fine-Tuning vs RAG: A Comprehensive Decision Framework
Last year, I faced a critical decision: fine-tune our LLM or implement RAG? We chose fine-tuning. It was expensive, time-consuming, and didn’t solve our core problem. After building 20+ LLM applications, I’ve learned when to use each approach. Here’s the comprehensive decision framework that will save you months of work. Figure 1: Fine-Tuning vs RAG […]
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