AI Research Agents: How They Search, Analyze, and Synthesize Information
Learn how AI research agents plan searches, evaluate evidence, synthesize findings, and produce cited reports with human review.
Rerun Blog12 min read
retrieval augmented generation
507 stories
Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom
Autonomous driving decision systems must balance safety, efficiency, and social norms in complex traffic interactions. Philosophical and ethical considerations have received limited attention in existing autonomous driving decision-making approaches based on numerical optimization, sequence…
arXiv - cs.AI2 min read
pgvector for RAG: When you don’t need a dedicated vector database
Not every RAG stack needs another database. If your application already runs on PostgreSQL, pgvector may be enough for storing embeddings, filtering results, and running semantic search.
dbForge AI Assistant Blog10 min read
From Retrieval to Governance — The Architecture Shift Replacing Naive RAG
From Retrieval to Governance — The Architecture Shift Replacing Naive RAG Stop asking “How do we retrieve more chunks?” Start asking “How do we govern and structure what the model …
Towards AI12 min read
Agentic context layers: 64% of enterprises traced a confidently wrong AI agent answer to problems in their own company data in the past six months
VentureBeat Intelligence surveyed respondents at organizations with 100 or more employees in August 2026. We asked where their AI agents get business context and whether those agents had given wrong answers traced to that context, once or more than once.
VentureBeat AI18 min read
Inference, optimized: How we benchmarked Runpod Overdrive
See how Runpod benchmarked four models across sixteen workload profiles to measure Overdrive inference performance, efficiency, and scaling behavior.
RunPod4 min read
Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases
Build a Retrieval Augmented Generation (RAG) application on Amazon Bedrock Managed Knowledge Base with LangChain, and see how agentic retrieval handles the multi-part questions that single-shot retrieval answers poorly.
AWS ML Blog15 min read
Connecting AI agents to enterprise knowledge
For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. More than data, knowledge is the understanding of what the data means in the context of individual organizations.
MIT Tech Review - AI3 min read
The LLM Blindspot: Why Models Forget What’s in the Middle of Your Prompt
In this article, we’ll look at why LLMs have this bias against middle information.
ByteByteGo — System Design and Tech12 min read
Computer Vision: SIFT algorithm (Scale Invariant Feature Transform)
Elegant object matching from several viewpoints The post Computer Vision: SIFT algorithm (Scale Invariant Feature Transform) appeared first on Towards Data Science.
Towards Data Science13 min read
AI Helped Me Build RAG. I Still Couldn’t Debug It.
What retrieval failures taught me and why I built a small Python exercise that fails on purpose.Continue reading on Towards AI »
Towards AI1 min read
Vector Search Is Not Enough: The Blueprint of GraphRAG
Vector Search Is Not Enough: The Blueprint of GraphRAG Vector search tells you what is similar; GraphRAG tells you how it is connected. Retrieval-Augmented Generation (RAG) has emerged as an …
Towards AI11 min read
Building an Enterprise AI Customer Support Platform with Claude Fable 5.1 and Claude Code
Most AI coding demos stop at task managers, weather apps, or simple chatbots. For this project, we take on something more demanding: building an enterprise customer-support platform that can investigate complaints, retrieve relevant policies, recommend resolutions, and keep risky actions behind…
Analytics Vidhya20 min read
RAG Evaluation Metrics: Formulas and a Worked Example
RAG evaluation metrics with formulas and a worked example: precision@k, recall@k, MRR, nDCG, faithfulness and answer relevancy, plus why frameworks disagree.
TestMu AI Blog33 min read
Presentation: Building Reusable Evaluation Frameworks for Agentic AI Products
Susan Chang explains how Elastic transitioned from siloed, ad-hoc AI agent evaluations to a unified, production-grade framework. She discusses balancing LLM-as-a-judge with deterministic rules, bridging Python data science evals with TypeScript production code, and implementing deep tracing to…
InfoQ - AI/ML/Data32 min read
How to Build a Cheap, Yet Reliable Model Router With Jev
Model routing can finally be a design choice rather than a system enhancement The post How to Build a Cheap, Yet Reliable Model Router With Jev appeared first on Towards Data Science.
Towards Data Science7 min read
Complete Guide to Build With An AI Software Engineer -Devin AI
You can check out Devin Ai here:-https://get.aspr.app/SH21SY What can Devin, Cognition’s AI software engineer, actually do? In this video, we put Devin through a real engineering workflow by building an Agentic RAG system from end to end — while exploring the capabilities that make Devin more than…
Krish NaikOutput Language Confusion under Multilingual Prompt Contamination
Standard factual benchmarks assume clean monolingual prompts and exact-match scoring, two assumptions that break simultaneously in real-world multilingual deployment, from retrieval-augmented generation pipelines returning mixed-language passages to users pasting multilingual web content.
arXiv - cs.CL (NLP)2 min read
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