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Campus AI tutors based on retrieval-augmented generation (RAG) must ground answers in assigned course materials while keeping textbooks and student dialogue on institutional infrastructure.
World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based environments, fine-tuning a Language Model (LM) to serve as a WM has emerged as a dominant paradigm.
Issue #4 of The State of Serene: faster search on every core and from a cold cache, regex and fuzzy search that always answer, text analyzers as expressions, HNSW, the docs inside the binary, OpenTelemetry ingestion, faster time histograms, MATCH_RECOGNIZE, spatial and AI functions.
The short answer An AI pipeline can look healthy at the infrastructure level while the actual AI system is failing. A model can be available,...
Originally published on tamiz.pro. You added another 2,000 tokens of coding conventions to your system prompt. Your agent now follows the style guide... but it also ignores your bug-fix instructions, hallucinates import paths, and produces function bodies that are 40% longer than necessary.
Learn why vector RAG fails at multi-hop reasoning and how combining Neo4j Knowledge Graphs with Gemini creates auditable enterprise AI.
The support ticket was one line long: "Your assistant says our docs don't mention refunds. We have an entire page called Refunds." I checked. The page was there. It was chunked, embedded, and sitting in Postgres with the right tenant_id.
An agent piped a wiki export through my HTML-to-Markdown converter into a RAG pipeline and reported that hierarchy was disappearing. Every nested task list came out flat: sub-items rendered as siblings of their parents.
Imagine asking your AI assistant about last quarter's compliance report and getting a precise answer, sourced directly from your internal documents, without that data ever leaking to a public model. This is no longer science fiction—it's RAG (Retrieval-Augmented Generation) in production.
Imagine um assistente de IA capaz de responder perguntas sobre contratos, políticas internas e manuais da sua empresa — com a precisão de quem leu cada documento e a discrição de quem nunca vaza informação confidencial. Esse cenário deixou de ser ficção.
Everyone's building agents right now. But a better question than "can an agent do this?" is "when does an agent actually beat something simpler?" For the TigerGraph Agentic GraphRAG Hackathon, I built three question-answering pipelines side by side to find out.
An end-to-end guide to deterministic checks, retrieval metrics, human review, LLM judges, and regression testing, with practical examples from a local multi-agent RAG application.
Limit the agent to the conversation that opened the task, keep a citation on every message you use, and search in passes before you summarize. For a project catch-up, Jaime DeLanghe described one keyword spinning off five related searches, then a cluster of interest, then a pared-down set, then the…
From Comics to Code: Building MyZubster One Piece of Evidence at a Time It started with a comic. N4K48 visually represented a journey: a software idea taking shape and imagining its path into the MyZubster metaverse. Those panels were not screenshots of a finished product.
Why adding an LLM changes the security model of an ordinary application Adding an LLM to an existing application can look deceptively simple. A user sends a request. The application sends it to a model. The model returns an answer.
In this video: 0:00 Do 1M windows end choosing? No 0:19 Prompt engineering is half right 1:25 Two reflexes: polish or dump 2:34 Personas, stuffing and the experiment 5:15 Selection, order and length 8:15 Tools, schemas and examples 10:58 Costs, and when stuffing is fine 12:37 Prompt, context and…
Today, we’re starting our journey, on DEV.to. We’re a software and AI agency focused on building practical technology that helps businesses work smarter, automate repetitive processes, and create better digital experiences.
#RAG #ChunkEnrichment #AIEngineer Curated Structured Learning program: https://balajichippada.com/ 🌐 Free AI Engineer Roadmap 2026: https://balajichippada.com/roadmap 🔗 Join the WhatsApp Community https://chat.whatsapp.com/EoQWZa870wG3TzUeOpH54c This is to bring together serious and like-minded…
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