DataScientists: a blog about everything data related.
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RAG Context Pruning for Efficiency and Cost Optimization
After baseline production runs across our clients’ financial discovery pipelines, we observed an increase in Time-to-First-Token (TTFT) when retrieved context exceeded 2,500 tokens. Furthermore, the system’s retrieval accuracy score decayed when the target information was located in the middle 40% of the injected payload. We addressed this bottleneck by deploying an inline sentence-level extractive context…
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Production-Grade Compliance: Engineering the EU AI Act into Sovereign Agentic Pipelines
We measured a 42% increase in inference latency when we shifted from standard RAG to a cryptographically-verifiable audit chain. We accept this overhead. After 2,000 simulated audit requests, we verified that any response lacking a signed Model_Hash and Data_Snapshot_ID could be purged within 150ms, effectively hardening the system against the “Black Box” failure modes targeted…
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Unified Graph-RAG in a Single Postgres Engine
Our production benchmarks confirm that consolidating Hybrid Graph-RAG into a single PostgreSQL instance via pgvector and Apache AGE reduced cross-service network latency and eliminated the consistency lag inherent in multi-database synchronization. The Unified Postgres Architecture We enforce a unified data layer by storing vector embeddings and graph property data within the same relational clusters. This…
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Production Metric: 14.2% Semantic Decay
After processing 2.8 million unstructured retail fragments, we observed that 14.2% of records passing traditional NOT NULL and regex constraints contained semantic noise specifically CAPTCHA text, “out of stock” redirects, and promotional modals that poisoned downstream RAG embeddings. We enforced a deterministic quality gate using PydanticAI and a sovereign vLLM cluster, which suppressed these failures…
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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…
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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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The Future of Automation is Local: Why German Firms are Trading the Cloud for On-Premise AI
In early 2026, the AI landscape reached a crossroads. On one side, we have the “reasoning giants”: GPT-5.4 and Gemini 3.1 Pro. These models offer unprecedented cognitive abilities, but they come with a “Data Tax” that many German firms are no longer willing to pay. On the other side, a revolution in Small Language Models…
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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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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…
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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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