Paid tutorial: calculate rank fusion, Recall, MRR and Jaccard, then check whether citations support claims. Worked examples and exercises use physics RAG to teach evaluation, not reanalyse the original search logs.

https://note.com/hydezero/n/nda704916cc74?hl=en
#Physics #RAG #Research

물리 문헌 RAG의 SPECTER2·BM25·순위 융합·재순위화를 따라갑니다. Recall@10은 합성 질문에서 원래 논문 한 편을 찾는 지표이며 보고서 정확성과 다릅니다. 제목·초록 검색 뒤에는 본문 검증이 필요합니다.

https://note.com/hydezero/n/na3ae20fd2064?hl=ko
#물리학 #RAG #연구

Physics literature RAG: trace SPECTER2, BM25, rank fusion and reranking. Recall@10 tests recovery of one source paper from synthetic queries, not report accuracy. Title-and-abstract search still needs full-text checks.

https://note.com/hydezero/n/na3ae20fd2064?hl=en
#Physics #RAG #Research

AI는 필요한 물리 논문을 어떻게 찾을까요? Kumar·Kumar의 단어·의미 검색 결합을 살펴봅니다. 합성 질문에서 원래 논문 한 편을 찾는 성적은 생성 보고서의 정확성이나 문헌의 완전한 수집을 보장하지 않습니다.

https://note.com/hydezero/n/nd053d0be0ca0?hl=ko
#물리학 #AI #RAG

Can AI find the physics papers you need? Kumar & Kumar combine keyword and semantic search. Recovering one source paper from a synthetic question does not guarantee accurate reports or complete coverage.

https://note.com/hydezero/n/nd053d0be0ca0?hl=en
#Physics #AI #RAG

物理文献RAGの検索評価を読む。BM25と意味検索の順位融合、再順位付け、RecallとMRRを整理します。合成質問ごとの正解は元論文1本。検索の得点は生成レポートの正確さを保証せず、収録・質問件数の記述差も残ります。
https://note.com/hydezero/n/na3ae20fd2064
#RAG #情報検索 #物理学

🤖How do embeddings help Generative AI?

🧠They turn information into vectors that power:
🔹 Semantic search
🔹 RAG applications
🔹 Smart recommendations
🔹 Context retrieval

🚀Explore the tools, benefits & challenges!
👉Read the full article: www.linkedin.com/pulse/how-do...

#GenAI #AI #Embeddings #RAG

判断特化型GraphRAGをJevを使って構築してみた（ベクトル未使用）

#rag #AWS #GraphRAG #DynamoDB #Jev

"Ragas vs Truvec for RAG Retrieval Evaluation" by Abdesslem Amri

#rag #retreival-augmented-generation #ai

RAG com abordagem baseada em árvore de decisão ao invés de base de dados vetorial.

PageIndex: github.com/VectifyAI/Pa...

#RAG #decisiontree #vectordb #AI #IA

MLPerf Inference v6.1: What the New RAG and Agent Benchmarks Mean for Your AI Costs

MLPerf Inference v6.1, released by MLCommons on September 16, 2026, added a test that times a full…

http://digitalmatters.me/it-infrastructure/mlperf-inference-v6-1/

#AI_Hardware #AI_Inference #RAG

For developers fine-tuning code retrievers, MAP4CS prunes training data using syntactic, semantic, and distributional signals to make the process cheaper and more effective than full-corpus or random sampling.…

#AI #RAG #DeveloperTools #SoftwareEngineering
https://arxiv.org/abs/2610.11727

Mastering AI Citations: How Answer Engines Pick Their Authoritative Sources

Reverse-engineering RAG pipelines: How Perplexity, ChatGPT Search, and Gemini evaluate source authority, vector similarity, and factual triangulation to choose citation links.

#AICitations #Perplexity #ChatGPTSearch #RAG

Технология RAG: как внешние данные делают ИИ точнее и надежнее

Метод генерации с дополненным поиском (RAG) объединяет возможности больших языковых моделей с доступом к внешним базам знаний для создания достоверного контента.

Читать полностью

#ИскусственныйИнтеллект #RAG #МашинноеОбучение

Глубокое погружение в векторные БД: гид для тех, кто уже создал свою RAG-систему

Новая книга «Vector Databases» помогает разобраться в механизмах семантического поиска, превращая «черный ящик» извлечения данных в понятный инструмент.

Читать полностью

#RAG #ВекторныеБазыДанных #ИИ

🤖How do you measure RAG performance?

Explore key RAG Evaluation Metrics:
✅ Context Precision & Recall
✅ Faithfulness
✅ Answer Relevancy
✅ Answer Correctness
✅ Latency & Cost

🚀 Build more reliable AI applications!
Read the full article: www.linkedin.com/pulse/what-k...

#RAG #GenerativeAI #LLM #AI

🚀 #Agentic RAG vs. Standard #RAG: What's the Real Difference?

🎯 Learn Python, RAG, LangChain & AI Agents with Visualpath's Agentic AI Training!

Read Full Blog: visualpathblogs.com/agentic-ai/a...

📅 New Batch Starts: 12 October
📞 For More Information: +91 7032290546

生成AIセキュリティ：攻撃手法と防御策を体系的に理解する

#生成AI #Security #プロンプトインジェクション #rag #LLM

"Del RAG tradicional al RAG agentico en AWS: evolución, patrones y arquitectura de referencia" by Jose Luis Ariza

#rag #artificial-intelligence #agentic-ai #amazon-bedrock #aws-lambda

"Structured Extraction Pipeline" by Chikkudu Nithin

#pipeline #aws-builders #amazon-bedrock #rag