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Data · Tuesday, 4 August 2026 · Issue 65 · 6 stories

Meta Doubles Training Efficiency, Datatype Font Charts in Text

Analytics & Visualization

ML & AI for Data

RAG pipelines need three engineering layers: prompt, context, and loop

The article breaks every Retrieval‑Augmented Generation (RAG) system into three engineering layers, prompt, context, and loop. Prompt engineering defines the system and user messages, context engineering decides what data fills the model’s window, and loop engineering orchestrates multi‑call retries and termination. Mastering these layers lets teams build scalable, reliable enterprise RAG solutions.

towardsdatascience.com ↗22 min read at source

Meta Doubles Ads Model Training Efficiency at LLM Scale

Meta's Generative Ads Recommendation Model (GEM) now trains on thousands of latest GPUs, achieving 20‑25% model FLOPs utilization and a 4× increase in training FLOPs, effectively doubling end‑to‑end efficiency. They did this via custom kernels, ultra‑low‑precision training, and a 5‑dimensional topology‑aware parallelism stack.

engineering.fb.com ↗24 min read at source

Liquid AI’s 2.6B LFM model brings edge‑ready AI agents

Liquid AI’s LFM2.5‑2.6B packs 2.6 B parameters into a model that runs on‑device with under 2.5 GB RAM, delivering 220 tokens/s on an Apple M5 Max and 113 tokens/s on a Ryzen CPU. Trained with a four‑stage agentic pipeline, it matches or beats 4‑× larger models on tool use, instruction following, and multi‑step tasks, opening the door to edge‑deployed AI agents without cloud reliance.

huggingface.co ↗4 min read at source

You’re caught up.

That was all 6 stories in the 4 August issue.