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#Embeddings Reel by @the.poet.engineer - exploring shapes of thoughts: extracted my personal notes' embeddings and arranges them as a 3d network using 3 different topologies. project files a
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@the.poet.engineer
exploring shapes of thoughts: extracted my personal notes’ embeddings and arranges them as a 3d network using 3 different topologies. project files and tutorial available on patreon #touchdesigner #touchdesignerlearning #touchdesignercommunity #newmediaart #creativecoding
#Embeddings Reel by @deeprag.ai - Inside every Transformer model is a hidden geometry lesson. 📐🤖 When we talk about token embeddings in Transformer architectures, we're really talki
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@deeprag.ai
Inside every Transformer model is a hidden geometry lesson. 📐🤖 When we talk about token embeddings in Transformer architectures, we’re really talking about mapping words into a high-dimensional vector space where meaning becomes math. Each token is converted into a dense vector. Words that share semantic meaning cluster together. Similarity isn’t guessed. it’s measured through dot products and cosine similarity. What makes this powerful is structure. Relationships between words are preserved as directional offsets in the vector space. That’s why the classic example works: King − Man + Woman ≈ Queen This isn’t magic. It’s linear algebra powering large language models like GPT, Gemini, and Claude. Embeddings are the foundation of modern NLP, semantic search, recommendation systems, and generative AI. They transform language into geometry and geometry into intelligence. Credits: 3blue1brown Follow @deeprag.ai for deep dives into Transformers, embeddings, machine learning, and the math behind artificial intelligence. . . . . . . #ArtificialIntelligence #MachineLearning #DeepLearning #Transformers #NLP LLM VectorEmbeddings LinearAlgebra DataScience AIExplained GenerativeAI TechEducation
#Embeddings Reel by @codewithbrij (verified account) - Vectorless RAG is quietly changing how we build retrieval systems.And most engineers haven't noticed yet. For 3 years we've been told RAG = embedding
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@codewithbrij
Vectorless RAG is quietly changing how we build retrieval systems.And most engineers haven't noticed yet. For 3 years we've been told RAG = embeddings + vector database. Chunk your docs. Embed everything. Store in Pinecone. Similarity search at query time. It works. But the baggage is real → → Embedding drift across model versions → Chunk size tuning that never feels right → Semantic search that misses exact keywords → Vector store infrastructure costs → Re-indexing nightmares when your model changesVectorless RAG skips all of that.BM25 for keyword precision. Knowledge graphs for relationship reasoning. SQL retrieval for structured data. Long context LLMs that skip indexing entirely. The real unlock in 2026? Hybrid systems.One query. Multiple retrievers. Route to the right method at runtime. Best result wins.Stop defaulting to vector search because the tutorial said so. Start asking → what does my data actually need? Save this and send it to an engineer who's still embedding everything 🔥. . .#RAG #AI #MachineLearning #LLM #VectorDatabase #AIEngineering #GenerativeAI #ArtificialIntelligence #TechContent #DataEngineering #NLP #BuildInPublic #AIArchitecture #DeepLearning #MLOps
#Embeddings Reel by @sayed.developer (verified account) - What is a vector database 🤔 A vector database stores data as numerical embeddings (vectors) that represent meaning rather than exact text or values.
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@sayed.developer
What is a vector database 🤔 A vector database stores data as numerical embeddings (vectors) that represent meaning rather than exact text or values. It enables similarity search by finding items that are mathematically close to a query vector instead of using exact matches. In short: vector databases power semantic search, recommendations, and AI retrieval by understanding context and meaning.🫡🤝 #softwareengineering #computerscience
#Embeddings Reel by @priyal.py - Step 1 - Transformers Basics Learn pipelines, AutoModels & pretrained models Step 2 - Tokenization Understand BPE, attention masks & padding Step 3
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@priyal.py
Step 1 - Transformers Basics Learn pipelines, AutoModels & pretrained models Step 2 - Tokenization Understand BPE, attention masks & padding Step 3 - Embeddings Convert text into vectors for search & RAG Step 4 - Fine-Tuning Train models on your own datasets Step 5 - HF Datasets Load, preprocess & stream large datasets Step 6 - RAG Pipelines Combine retrieval + LLM reasoning Step 8 - LLM Inference Run TinyLLaMA, Mistral & optimize prompts Step 8 - Deployment Share models using Spaces & the Hub #datascience #machinelearning #learningtogether #womeninstem #progresseveryday #tech #consistency #ai #generativeai
#Embeddings 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
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@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
#Embeddings Reel by @aibutsimple - Cosine similarity measures how similar two vectors are by comparing the angle between them, making it especially useful for high-dimensional represent
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@aibutsimple
Cosine similarity measures how similar two vectors are by comparing the angle between them, making it especially useful for high-dimensional representations like embeddings (used in attention). In transformers, this idea is closely related to how attention works: query and key vectors are compared using a dot product, which effectively captures how aligned or relevant two tokens are to each other. When two vectors point in similar directions, their similarity is high (closer to 1), meaning one token should pay more attention to the other. These similarity scores are then normalized and used to weight the value vectors, allowing the model to selectively focus on the most relevant parts of a sequence. C:3blue1brown Want to Learn In-Depth Machine Learning Topics? Join 8000+ Others in our Visually Explained Deep Learning Newsletter (link in bio). Need beautiful, technically accurate visuals for your business? From full slide decks to newsletter design, we handle everything. Join our AI community for more posts like this @aibutsimple 🤖
#Embeddings Reel by @iitian_decodes - Comment "llm" for direct link! Here's what's inside: Foundation → Advanced → Language model basics → Tokens and embeddings → Transformer architecture
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@iitian_decodes
Comment “llm” for direct link! Here’s what’s inside: Foundation → Advanced → Language model basics → Tokens and embeddings → Transformer architecture explained → Text classification techniques Real-World Applications → Semantic search systems → RAG implementation guide → Prompt engineering mastery → Multimodal LLM usage Build Your Own → Create embedding models → Fine-tune BERT yourself → Train generation models → Deploy production systems Written by Jay Alammar. Endorsed by Andrew Ng.
#Embeddings Reel by @techwithprateek - I stopped consuming and started building a few small systems end to end. 🧠 The first shift happened when I built a simple RAG app on my own document
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@techwithprateek
I stopped consuming and started building a few small systems end to end. 🧠 The first shift happened when I built a simple RAG app on my own documents. Suddenly, hallucinations weren’t an abstract problem anymore. I could see exactly why they happened, what embeddings actually do, and how retrieval changes the quality of answers. It was the first time LLMs stopped feeling magical and started feeling mechanical. 🤖 Then I built a social media AI agent with approvals in the loop. That’s when “agentic AI” clicked for me. Not as a buzzword, but as a workflow problem. When to wait, when to act, when to ask a human. Integrating AI with real systems taught me more than any prompt guide ever could. 📈 A stock market assistant broke another illusion. I learned very quickly that language models are not math engines. Numbers need computation, not vibes. Combining LLM reasoning with actual data pipelines made the limits painfully obvious — and that’s a good thing. 🧠 Adding memory to an assistant was quieter but deeper. Managing context, summarizing past interactions, deciding what to remember and what to forget. That’s when I understood why some tools feel personal and others feel shallow. 🧪 Finally, I built a data quality copilot. Upload data, let AI reason over SQL outputs, and generate a report. This one felt closest to real enterprise work. Multi-step reasoning, messy data, and outputs that actually need to be trusted. The big realization was simple: learning AI isn’t about learning prompts. It’s about building workflows. When you build systems, you understand limits, architecture, and trade-offs. That’s the level companies actually care about. 💾 Save this if you’re tired of passive learning 💬 Comment “AI PROJECTS” if you want GitHub-ready structures 👣 Follow for more grounded takes on AI and data
#Embeddings Reel by @bloomtechofficial (verified account) - How does ChatGPT understand you? (AI Basics Pt. 4 - Embeddings) #ai #aibasics #artificialintelligence #coding #programming #codingnews #openai #meta
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@bloomtechofficial
How does ChatGPT understand you? (AI Basics Pt. 4 - Embeddings) #ai #aibasics #artificialintelligence #coding #programming #codingnews #openai #meta #llama #llama3 #chatgpt #bloomtech #aifordeveloperproductivity #learnai #machinelearning #ml #learntocode #softwareengineering #swe #githubactions
#Embeddings Reel by @touchdesigner - repost @the.poet.engineer exploring shapes of thoughts: extracted my personal notes' embeddings and arranges them as a 3d network using 3 different t
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@touchdesigner
repost @the.poet.engineer exploring shapes of thoughts: extracted my personal notes’ embeddings and arranges them as a 3d network using 3 different topologies. project files and tutorial available on patreon #touchdesigner #touchdesignerlearning #touchdesignercommunity #newmediaart #creativecoding
#Embeddings Reel by @the_enterprise.ai - RAG Embeddings Best Practices #artificialintelligence #rag #aiagents #programming #claude
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@the_enterprise.ai
RAG Embeddings Best Practices #artificialintelligence #rag #aiagents #programming #claude

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