Implementing Hybrid Semantic-Lexical Search in RAG

Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted. The post Baseline Enterprise RAG, From PDF to Highlighted Answer appeared first on Towards Data Science.

Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality. The post RAG Is Burning Money — I Built a Cost Control Layer to Fix It appeared first on Towards Data Science.

For AI engineers who want to understand every step, not just call the library The post Enterprise Document Intelligence: A Series on Building RAG Brick by Brick, from Minimal to Corpus scale appeared first on Towards Data Science.

Vector databases are now core retrieval infrastructure for RAG and agentic AI. This guide compares nine production options on architecture, pricing, and scale. The post Best Vector Databases in 2026: Pricing, Scale Limits, and Architecture Tradeoffs Across Nine Leading Systems appeared first on MarkTechPost.

Three weeks into testing, a learner told me my AI tutor gave her the wrong answer. Not obviously wrong — just outdated enough to mislead. That was the moment I realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most current one. And in a knowledge base that changes constantly, that’s a serious flaw. The fix wasn’t in the retriever or the model. It was in the gap between them. I built a temporal layer that filters expired facts, boosts time-sensitive signals, and makes the system prefer what’s still true — not just what matches. The post RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production appeared first on Towards Data Science.

RAG is a model that connects large language models to live agency knowledge bases — enabling grounded, mission-specific responses, rather than generic outputs.

Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and images in a single […] The post Gemini API File Search: The Easy Way to Build RAG appeared first on Analytics Vidhya.

Your RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations before they reach users. The post RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time appeared first on Towards Data Science.