DATA DO – データ 道

Tag: RAG

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • 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…

  • Building Production-Grade Agentic RAG: A Technical Deep Dive – Part 1

    Beyond Fixed Windows — Agentic & ML-Based Chunking Introduction: The RAG Gap The promise of Retrieval-Augmented Generation (RAG) is compelling: ground large language models in enterprise data, reduce hallucinations, enable real-time knowledge updates. But in practice, most RAG systems fail silently. They fail not because embedding models are weak or vector databases are slow, but…

  • The Ultimate Vector Database Showdown: A Performance and Cost Deep Dive on AWS

    In the age of AI, Retrieval-Augmented Generation (RAG) is king. The engine powering this revolution? The vector database. Choosing the right one is critical for building responsive, accurate, and cost-effective AI applications. But with a growing number of options, which one truly delivers? To answer this, we put five popular AWS-hosted vector database solutions to…