LLMOps

Table of Contents Introduction RAG 101: Foundations of Retrieval‑Augmented Generation Why Classic RAG Falls Short in Production Enter Agentic RAG: The Next Evolution Core Architecture of an Agentic RAG System 5.1 Retriever Layer 5.2 Planner / Orchestrator 5.3 Executor LLM 5.4 Memory & Knowledge Store Designing Autonomous Retrieval Loops Practical Implementation with LangChain & LlamaIndex Scaling Agentic RAG for Production 8.1 Observability & Monitoring 8.2 Latency & Throughput Strategies 8.3 Cost Management 8.4 Security, Privacy, and Compliance Real‑World Deployments 9.1 Customer‑Support Knowledge Assistant 9.2 Enterprise Document Search 9.3 Financial Data Analysis & Reporting Best Practices, Common Pitfalls, and Mitigation Strategies Future Directions: Towards Self‑Improving Agentic RAG Conclusion Resources Introduction Retrieval‑augmented generation (RAG) has become a cornerstone technique for building LLM‑powered applications that need up‑to‑date, factual information. By coupling a retriever (often a dense vector search over a knowledge base) with a generator (a large language model), developers can produce answers that are both fluent and grounded in external data. ...

The landscape of Artificial Intelligence is shifting from simple, stateless chat interfaces to complex, autonomous agentic workflows. While single-agent systems can handle basic tasks, production-grade applications often require a “team” of specialized agents working together. This is where Multi-Agent Orchestration becomes critical. In this guide, we will explore how to master multi-agent systems using LangGraph, a library built on top of LangChain designed specifically for building stateful, multi-actor applications with LLMs. ...

The era of “prompt engineering” as the primary driver of AI utility is rapidly coming to a close. While crafting the perfect system message was the breakthrough of 2023, the industry has shifted toward Agentic Orchestration. We are moving away from single-turn interactions toward autonomous loops, and the most sophisticated way to manage these loops is through Neuro-Symbolic Frameworks. In this post, we will explore why the industry is moving beyond simple prompting and how you can leverage open-source neuro-symbolic tools to build resilient, predictable, and highly capable AI agents. ...

Large Language Models (LLMs) power transformative applications in generative AI, from chatbots to content generation. AWS provides a robust ecosystem—including Amazon Bedrock, Amazon SageMaker, and specialized infrastructure—to build, train, deploy, and scale LLMs efficiently.[6][1] This guide dives deep into AWS services for every LLM lifecycle stage, drawing from official documentation, best practices, and real-world implementations. Whether you’re defining use cases, training custom models, or optimizing production deployments, you’ll find actionable steps, tools, and considerations here. ...

Table of Contents Introduction Understanding LLMs and Azure’s Role Azure Machine Learning for LLMOps The LLM Lifecycle in Azure Data Preparation and Management Model Training and Fine-Tuning Deploying LLMs on Azure Advanced Techniques: RAG and Prompt Engineering Best Practices for LLM Deployment Monitoring and Management Resources and Further Learning Conclusion Introduction Large Language Models (LLMs) have revolutionized artificial intelligence, enabling organizations to build sophisticated generative AI applications that understand and generate human-like text. However, deploying and managing LLMs at scale requires more than just powerful models—it demands robust infrastructure, careful orchestration, and operational excellence. This is where LLMOps (Large Language Model Operations) comes into play, and Azure Machine Learning provides the comprehensive platform to make it all possible. ...