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Developers Build Hybrid RAG Pipelines Using FAISS, BM25 and Qwen

Practical projects show retrieval quality determines whether LLM answers are grounded because poor chunking, weak embeddings, or naive ranking produce incorrect outputs.

Google Research Unveils EnvHarness to Convert Static Benchmarks Into Adaptive Training Environments

Meituan Releases LongCat-2.0, a 1.6 Trillion‑Parameter Model Trained on Chinese Chips

JetBrains Open-Sources Mellum2, a 12B Mixture-of-Experts Model

Gemma 4 Emerges as a Local-First Alternative to Cloud AI

KumoRFM-2 Brings Plain-English Predictions to Enterprise Relational Data

RAG Practice Coalesces Into a Seven-Step Playbook Focused on Retrieval

RAG Matures for Enterprise Use With On‑Prem Pipelines and Smarter Embedding Strategies