Tag: LLM
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Specialized Judges: Scaling RAG Evaluation with Prometheus-2 and PydanticAI
Our production benchmarks utilize the Feedback Collection and Preference Collection datasets to establish the performance delta between generalist and specialized evaluators. We observed that Prometheus-2 (8x7B) achieves a Pearson correlation of $0.898$ with human-annotated ground truth, which is on par with GPT-4 ($0.882$) and significantly higher than previous iterations of small generalist models. By enforcing…
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From Generalist to Specialist: Benchmarking the 25x Speedup of Fine-Tuned “Tiny Compilers”
We measured a 96.7% reduction in inference latency by migrating our EDI logic from Llama 4 (70B) to a fine-tuned Llama 3.2 (1B) “Tiny Compiler.” In high-volume logistics testing, the generalist model averaged 2,800ms per transaction, while the specialized 1B model, quantized to 4-bit, stabilized at $92ms$ on consumer-grade hardware. We accept the 0.4% decay…
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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…