#Embeddings

AI is transforming industries, but keeping up with its terminology is crucial. This video breaks down Prompt Engineering, System Prompts, Context Windows, Tokenization, and Vectors, helping you navigate the AI landscape with confidence. #AI #ArtificialIntelligence #MachineLearning #DeepLearning #Tech #Innovation #DataScience #NLP #AITools #GenerativeAI #LLM #AIExplained #AIKeyTerms #PromptEngineering #Tokenization #ContextWindow #Embeddings #AITrends #BigData #Automation #FutureTech #AITutorial #TechEducation

RAG (Retrieval-Augmented Generation) — explained in the simplest way ever. If you’ve ever wondered how AI tools like ChatGPT use your data to give accurate answers, THIS is the architecture behind it. In today’s video, I break down: 🔹 Indexing 🔹 Query processing 🔹 Semantic search 🔹 Prompt augmentation 🔹 LLM generation All in one clean visual flow. Save this for later — this is RAG explained the right way. ⚡️ #AI #ArtificialIntelligence #MachineLearning #RAG #RetrievalAugmentedGeneration #LangChain #VectorDB #Embeddings #AIEducation #CactusAI #TechReels #AITutorial #AIBuilders #SemanticSearch

You can’t just upload a PDF into a model’s brain because a model doesn’t store knowledge like a hard drive. There’s no neat folder called “company docs” where you can drop a 50-page file and expect perfect recall forever. Neural networks store what they learn across millions or billions of parameters, spread out in ways that are hard to isolate or edit. So when people say “let’s just inject this document into the model,” they’re imagining a memory system that simply doesn’t exist. That’s why vector databases matter: they give the model external memory it can search when needed, without changing its internal weights. So the real choice is this: if you want permanence, retrain. If you want control, freshness, and factual accuracy, retrieve. Confusing those two leads to bad system design fast. I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀 #AI #LLM #RAG #Embeddings #VectorDatabase #AIEducation #GenerativeAI #MachineLearning #AIForBusiness #TechExplained #NeuralNetworks #ChatGPTTips

TF-IDF was the original NLP technique for extracting important keywords! #llm #ai #google #tokenization #sentence #nlp #embeddings #chatgpt

Easy captain obvious, everyone knows that #ai #buildwithai #rag #embeddings

Embeddings convert text into numbers, helping models understand language. 🔹 Word2Vec – Uses CBOW & Skip-gram to learn word relationships but creates static embeddings (same word, same vector). 🔹 GloVe – Learns embeddings from word co-occurrence stats, still static like Word2Vec. 🔹 ELMo – Introduces contextual embeddings using bi-directional LSTMs, meaning the same word can have different meanings. 🔹 Transformer-based embeddings – BERT, GPT, and others use self-attention for context-aware embeddings, powering tasks like sentiment analysis & question answering. From static to contextual, embeddings are the foundation of modern NLP #machinelearning #LLMs #Embeddings #DataScience #womeninstem #learningtogether #progresseveryday #tech #consistency

New state of the art embedding model, Instructor, for text is available! It accounts for task and domain when creating an embedding #datascience #machinelearning #embeddings #word2vec #sentencetransformers #huggingface

#InteligenciaArtificial #IA #AI #MachineLearning #DeepLearning #LLM #ChatGPT #OpenAI #IAGenerativa #GenerativeAI #PromptEngineering #AIAgents #RAG #VectorDatabase #Embeddings #NLP #Transformers #AIBrasil #TechBrasil

Embeddings & Vector DB – How AI understands your vibe Ever wondered how AI gets what you mean even when you don’t use the exact words? That’s where embeddings come in — they convert your text into numerical representations based on meaning, not just words. And those “meaning vectors”? They’re stored in a Vector Database — so when you ask a question, AI finds similar ideas, not just matching phrases. That’s why your AI assistant feels smart — because it’s not just searching, it’s understanding. #techwithbhagwat #techsales #llm #chatgpt #embeddings #vectordb

Generative AI feels complex. It’s not. You only need to understand two things: Embeddings and vector databases. Embeddings convert text into numbers called vectors. Text with similar meaning gets similar vectors. That’s why “dog” and “puppy” are close to each other even though the words are different. Vector databases store these vectors and help find the most relevant information fast. When you ask a question: • your question becomes a vector • similar vectors are retrieved • that data is given to the LLM • the answer is generated from real documents This is how systems avoid hallucinations. Instead of guessing, the model is grounded in your data. It’s like giving someone a textbook before asking them to answer. If you understand embeddings and vector databases, you understand generative AI. Everything else is built on top of this. Save this. #genai #llm #embeddings #vectordatabase #rag

Similarity might be the most powerful—and human—concept in AI. Instead of magic, large language models turn words, sounds, and images into vectors in massive multi-dimensional spaces. Then it’s all about measuring how close those vectors are. A similarity score of 1 means two vectors are perfectly aligned. A score of 0? Completely different. Negative numbers? Opposite directions. This is how AI compares text, matches images, and even links sounds—no matter how complex the data. It’s just math finding patterns of likeness. 🎙️ From Episode 32 of AI Snacks with Romy and Roby #AISnacks #VectorSpace #MachineLearning #SimilaritySearch #AIExplained #Embeddings #AIeducation #AItech #RomyAndRoby

Down another search rabbit hole… #embeddings #gpt