RAG (retrieval-augmented generation)

2026-10-26

OTHER · 1 SOURCE · InfoQ AI, ML & Data Engineering

InfoQ opens November 2026 cohorts for Certified AI Engineering and High-Performing Teams

InfoQ announced three live online certification cohorts in November 2026: AI Engineering with Adi Polak (Nov 12, PST) and Lan Chu (Nov 13, CET), and a High-Performing Teams cohort with Olimpiu Pop (Nov 17). The programs cover production AI topics such as retrieval-augmented generation (RAG), context and memory pipelines, agent orchestration and control, AI infrastructure and evaluations, and team operating models; sessions are live, not recorded, and certification requires participation and a capstone.

4.0

2026-10-05

RESEARCH · 1 SOURCE · arXiv cs.AI

Paired evaluation finds hosted Jev outperforms open-weight Laya on most System-1 agent decisions; audit revises claimed savings

The arXiv paper (arXiv:2610.02267v1) presents a paired, reproducible evaluation of two System‑1 decision models for agent harnesses—open-weight Laya and hosted Jev—across 11 decision points with 7,283 base cases and 6,640 robustness variants. Jev is significantly more accurate on 9 of 11 decision points (+10.8 to +46.0 percentage points); neither model beats chance on zero‑shot model routing and they tie on RAG relevance gating. The authors also self‑audit their pipeline and correct earlier deployment claims: a reported 23.9% pre-screen saving becomes 4.3% after accounting for omitted costs, gate accuracy was conflated with end‑to‑end quality (58% vs. 98%), thresholds were tuned in‑sample (held‑out misses up to 17%), and a channel‑native effect altered injection false‑positive rates; all raw outputs and analysis code are published at the linked GitHub repo.

7.0

2026-09-28

COMPANIES · 1 SOURCE · InfoQ AI, ML & Data Engineering

Forter presentation: how it trained 200 R&D staff to build AI agents in two weeks

At QCon AI, Forter principal engineer Ben Maraney described a two‑week internal sprint that gave ~200 R&D staff hands‑on experience building AI agents. The team lowered friction by running a custom MCP server, combining no‑code and code platforms, avoiding complex RAG setups, and coordinating with security and legal to speed adoption.

5.0

2026-09-12

CODING · 1 SOURCE · InfoQ AI, ML & Data Engineering

Presentation: From Retrieval to Reasoning — Building Production Agentic AI Systems with Knowledge Graphs

Cassie Shum argues that knowledge graphs are a foundational component for agentic AI systems and outlines four practical architectural patterns — context bundling, decision provenance, code as truth, and agent visibility. She also demonstrates an engineering harness built on a knowledge graph aimed at streamlining feedback loops, optimizing token usage, and improving system reliability.

5.0

2026-08-20

COMPANIES · 1 SOURCE · Mistral AI

Mistral launches Agentic Search retrieval layer in Search Toolkit and Libraries

Mistral says Agentic Search is a multi-step retrieval layer that lets models navigate, inspect, and verify information inside long, complex documents; it’s available via the Mistral Search Toolkit and built into Libraries in Studio and Vibe. Mistral reports benchmark gains including ~3x correctness on FinanceBench (26.7% → 86%), a +45.6 point improvement on OfficeQA Pro (6.3% → 51.9%), up to 39.6% reduction in p90 latency, and up to one-third lower token usage.