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#Rag

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トレンドリール

(12)
#Rag Reel by @pick_coldstone - I think Nita might be stealing them all #icecream #coldstone #rag
895.9K
PI
@pick_coldstone
I think Nita might be stealing them all #icecream #coldstone #rag
#Rag Reel by @dailydoseofds_ - 8 RAG architectures for AI Engineers 🧠 (explained with usage) 1️⃣ Naive RAG Retrieves documents purely based on vector similarity between query emb
78.8K
DA
@dailydoseofds_
8 RAG architectures for AI Engineers 🧠 (explained with usage) 1️⃣ Naive RAG Retrieves documents purely based on vector similarity between query embedding and stored embeddings. Best for: Simple, fact-based queries where direct semantic matching suffices. 2️⃣ Multimodal RAG Handles multiple data types (text, images, audio) by embedding and retrieving across modalities. Best for: Cross-modal retrieval tasks like answering text queries with both text and image context. 3️⃣ HyDE (Hypothetical Document Embeddings) Generates a hypothetical answer document from the query before retrieval. Best for: When queries are not semantically similar to documents. 4️⃣ Corrective RAG Validates retrieved results by comparing them against trusted sources (e.g., web search). Best for: Ensuring up-to-date and accurate information. 5️⃣ Graph RAG Converts retrieved content into a knowledge graph to capture relationships and entities. Best for: Enhanced reasoning with structured context alongside raw text. 6️⃣ Hybrid RAG Combines dense vector retrieval with graph-based retrieval in a single pipeline. Best for: Tasks requiring both unstructured text and structured relational data. 7️⃣ Adaptive RAG Dynamically decides if a query requires simple retrieval or multi-step reasoning chain. Best for: Breaking complex queries into smaller sub-queries for better coverage. 8️⃣ Agentic RAG Uses AI agents with planning, reasoning (ReAct, CoT), and memory to orchestrate retrieval from multiple sources. Best for: Complex workflows requiring tool use, external APIs, or combining multiple RAG techniques. 👉 Over to you: Which RAG architecture do you use the most? #ai #rag #machinelearning
#Rag Reel by @ai.with.andrew (verified account) - Comment "RAG" if you want a link. Apple might have just quietly fixed one of the biggest pain points in RAG. Their new system CLaRa compresses docume
103.5K
AI
@ai.with.andrew
Comment “RAG” if you want a link. Apple might have just quietly fixed one of the biggest pain points in RAG. Their new system CLaRa compresses documents into “memory tokens” and then generates a hypothetical ideal answer to query against them — instead of hoping your question sounds like the document. Because the query reasoner is trainable and directly connected to the database, it can improve its retrieval over a few runs, giving you better context with smaller models and far more efficient pipelines. It’s basically HyDE on steroids, built to make RAG faster, smarter, and cheaper. #ai #ainews #rag #apple #machinelearning
#Rag Reel by @mavenhq (verified account) - 3 videos to learn RAG. All completely free on Maven. #maven #rag #ai #llms
254.7K
MA
@mavenhq
3 videos to learn RAG. All completely free on Maven. #maven #rag #ai #llms
#Rag Reel by @page__520 - Amr Rag Ke Tara Ego Mone Kore:)🙂❤️‍🩹 . . . . . . . . . . . . . . #fyp #foryou #reels #instadaily #explore
29.0K
PA
@page__520
Amr Rag Ke Tara Ego Mone Kore:)🙂❤️‍🩹 . . . . . . . . . . . . . . #fyp #foryou #reels #instadaily #explore
#Rag Reel by @qalb_albayat - 🛐 ​সবাই মনে করে আমার ego বেশি but আমার প্রচুর rag ভাই "it's not ego"!
78.7K
QA
@qalb_albayat
🛐 ​সবাই মনে করে আমার ego বেশি but আমার প্রচুর rag ভাই "it's not ego"!
#Rag Reel by @kireee__bishal - Rag tai deklhen shudo, ar apnake chara jeh kichu bujhi na oida deklhen na kn? . . #instareels #highlights #viral #foryou #trending
3.3K
KI
@kireee__bishal
Rag tai deklhen shudo, ar apnake chara jeh kichu bujhi na oida deklhen na kn? . . #instareels #highlights #viral #foryou #trending
#Rag Reel by @aiwithtani - RAG is the difference between an LLM guessing and an LLM knowing. This is the week where everything starts to feel practical.#RAG #LLM #AIengineering
127.5K
AI
@aiwithtani
RAG is the difference between an LLM guessing and an LLM knowing. This is the week where everything starts to feel practical.#RAG #LLM #AIengineering #AIsystems #MachineLearningForStudents
#Rag Reel by @engineerbeatsai - Hallucinations - the biggest enemy of AI ⚠️ Sometimes LLMs give answers that sound perfect… but are completely made up. This is called AI hallucinat
568.2K
EN
@engineerbeatsai
Hallucinations - the biggest enemy of AI ⚠️ Sometimes LLMs give answers that sound perfect… but are completely made up. This is called AI hallucination. Why it happens: • LLMs predict the next likely word, not the truth • Missing or outdated knowledge • No real data to verify against That’s why modern AI systems use: ✔ RAG to retrieve real documents ✔ Tool calling to fetch live data ✔ Guardrails to verify answers Great AI systems don’t just generate answers. They check the data first. Follow @engineerbeatsai to master AI #GenAI #LLM #RAG #Hallucination #AIEngineering PromptEngineering AgenticAI AIForDevelopers EngineerBeatsAI
#Rag Reel by @cactuss.ai (verified account) - Learn about this concept, comment for Github link #rag #vector #artificialintelligence #deeplearning #machinelearning
1.1M
CA
@cactuss.ai
Learn about this concept, comment for Github link #rag #vector #artificialintelligence #deeplearning #machinelearning
#Rag Reel by @neatroots - The no.1 mistake people make while building RAGS is Optimizing embeddings while ignoring metadata filtering. Vector search answers *how similar* docu
213.8K
NE
@neatroots
The no.1 mistake people make while building RAGS is Optimizing embeddings while ignoring metadata filtering. Vector search answers *how similar* documents are. Metadata decides which documents are even allowed to compete. If you don’t pre-filter, your system: - Searches 2019 docs for 2025 questions ❌ - Wastes 75% of the context window ❌ - Adds +30ms latency for worse recall ❌ 🔍 Production smell test Query: *“What were our Q4 2025 cloud infrastructure costs?”* If results include 2023 docs → your RAG is broken. ✅ Production-grade fix: Staged Hybrid Filtering Stage 1 — Pre-filter (indexed, selective) - date_range, department, access level - 1M docs → 100K - +6ms latency, 95% recall Stage 2 — ANN vector search - Semantic ranking on the filtered subset (HNSW / IVF) Stage 3 — Post-filter (non-indexed) - author_verified, tags, word_count - Lightweight refinement only 📊 Real benchmarks (1M docs) - No filter: 12ms, wrong category - Post-filter: 45ms, 87% recall - Pre-filter: 18ms, 95% recall ✅ 💼 Interview-level insight “Pre-filter when selectivity <10%. Post-filter only when >50%. 10–50% → staged hybrid.” 📌 Takeaway Vector search finds meaning. Metadata enforces reality. Production RAG needs both. 🔖 Save this — metadata filtering separates builders from architects. #MachineLearning, #LLM #RAG #VectorSearch #HybridSearch [AIEngineering, OpenAI, MLOps, DataScience, ArtificialIntelligence, TechCareers, BigTech, Embeddings, SemanticSearch, NLP, DeepLearning, AIJobs, MLEngineer, ProductionML, SearchAlgorithms, RetrievalAugmentedGeneration, AIInterview, TechInterviewPrep, FAANG, SoftwareEngineering]

✨ #Rag発見ガイド

Instagramには#Ragの下に324K件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。

Instagramの膨大な#Ragコレクションには、今日最も魅力的な動画が掲載されています。@cactuss.ai, @pick_coldstone and @engineerbeatsaiや他のクリエイティブなプロデューサーからのコンテンツは、世界中で324K件の投稿に達しました。

#Ragで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。

人気カテゴリー

📹 ビデオトレンド: 最新のReelsとバイラル動画を発見

📈 ハッシュタグ戦略: コンテンツのトレンドハッシュタグオプションを探索

🌟 注目のクリエイター: @cactuss.ai, @pick_coldstone, @engineerbeatsaiなどがコミュニティをリード

#Ragについてのよくある質問

Pictameを使用すれば、Instagramにログインせずに#Ragのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。

パフォーマンス分析

12リールの分析

✅ 中程度の競争

💡 トップ投稿は平均701.5K回の再生（平均の2.4倍）

週3-5回、活動時間に定期的に投稿

コンテンツ作成のヒントと戦略

💡 トップコンテンツは10K以上再生回数を獲得 - 最初の3秒に集中

✍️ ストーリー性のある詳細なキャプションが効果的 - 平均長447文字

📹 #Ragには高品質な縦型動画（9:16）が最適 - 良い照明とクリアな音声を使用

✨ 多くの認証済みクリエイターが活動中（25%） - コンテンツスタイルを研究

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