Tag: GenAI
-
Cost-Aware Agentic Workflows with PydanticAI
Introduction: The Hidden Price of Autonomy The Architecture of a Cost Guardrail Implementing Usage Limits with PydanticAI PydanticAI provides the primary library-level enforcement mechanism through its UsageLimits class. Real-Time Cost Tracking with LiteLLM While PydanticAI manages counts, LiteLLM converts those counts to dollars. Detailed HITL Workflow: The Slack Intervention For a SMB, a simple notification…
-
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…
-
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…
-
The LLM-as-a-Compiler Pattern for High-Precision EDI Pipelines
As we look toward the next phase of industrial AI, the German Mittelstand is poised to move beyond “AI as a Chatbot” and toward the LLM-as-a-Compiler pattern. This represents a fundamental shift from “AI as a Librarian” to a “Deterministic Data Engineer.” The following architecture serves as a primary example of how this compiler pattern…
-
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…
-
Part 3: The Validation Layer — Reranking, Cross-Encoders, and Automated Evaluation
1. Introduction: Why Vector Search Alone Isn’t Enough In Part 2, we optimized our system for Recall—using expansion and routing to ensure the “needle” is somewhere in our top 50 results. However, in production, being “somewhere in the top 50” is a liability, not a feature. Vector search is fast—it takes milliseconds to retrieve candidates.…
-
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…
-
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…
-
How Poor Data Engineering Corrodes GenAI Pipelines
Generative AI (GenAI) has captivated the world with its ability to create, synthesize, and reason. From crafting compelling marketing copy to assisting in scientific discovery, its potential seems boundless. However, the dazzling outputs often mask a critical vulnerability: the quality of the data underpinning these systems. When data engineering falters, issues of data quality, governance,…
-
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…