Tag: LLM
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Part 4: The Human Interface — Enterprise RAG Deployment for 100+ Users
1. Introduction: From Prototype to Enterprise Building a Retrieval-Augmented Generation (RAG) system that works on a laptop is a common starting point, but it is rarely enough for a corporate environment. Consequently, deploying it to handle 100+ concurrent employees each with unique access levels, real-time streaming requirements, and finite GPU resources represents an entirely different…
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Part 2: The Multi-Step Retriever — Implementing Agentic Query Expansion
1. Introduction: The Death of the “Simple Search” In Part 1, we defined the blueprint for a production-grade Agentic RAG system. We moved away from passive retrieval toward a “reasoning-first” architecture. But even the best reasoning engine fails if the data fed into it is garbage. When a business user asks, “What’s our policy on…
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Modernizing Data Warehouses for AI: A 4-Step Roadmap
It’s the same conversation in every boardroom and Slack channel: “How are we using LLMs? Where are our AI agents? When do we get our Copilot?” But for the teams in the trenches, the hype is hitting a wall of legacy infrastructure. The truth is that Modernizing Data Warehouses for AI is the invisible hurdle…
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Designing Production-Grade GenAI Automation
A dbt Ops Agent Case Study A small, well-instrumented workflow can turn dbt failures into reviewable Git changes by combining deterministic parsing, constrained LLM tooling, and VCS-native delivery — while preserving governance through traces, guardrails, and CI. This is a blueprint to build a first Production-Grade GenAI Agent. You can find the complete implementation and…