Videos de llm
Videos etiquetados con "llm"
llm 22 videos
How Modern AI Systems Actually Work: RAG, Tool Calling & LLMs
In this MizuFlow.ai Foundation of Finance episode, Sung Lee, CFA, CPA, CA, explains the core architectural principles behind modern enterprise AI systems. Many organizations focus exclusively on the Large Language Model itself. However, real business value comes from combining: • LLM reasoning • enterprise data • tool calling • APIs • Retrieval Augmented Generation (RAG) • structured outputs • workflow orchestration This session explores how these components work together to create intelligent systems capable of solving real-world business problems. The objective is to help finance, accounting, and technology professionals understand how enterprise AI moves beyond simple chat interfaces into fully integrated operational platforms. 🧠 What This Video Covers Enterprise AI Is More Than an LLM A common misconception is that AI equals the model. In reality: Model ≠ System The model provides: • reasoning • language understanding • planning • decision support The application provides: • actions • integrations • workflows • business execution Real enterprise AI requires both. Local Inference vs Cloud AI The session compares two primary deployment approaches. Local Inference Models run on: • local servers • private infrastructure • enterprise-controlled environments Advantages: ✅ Privacy ✅ Data sovereignty ✅ Lower long-term inference costs Challenges: ❌ Hardware requirements ❌ Maintenance complexity ❌ Potentially weaker models Cloud APIs Examples include: • OpenAI • Gemini • Anthropic Advantages: ✅ State-of-the-art models ✅ Rapid deployment ✅ Minimal infrastructure Challenges: ❌ Ongoing API costs ❌ Data governance considerations ❌ Third-party dependencies The Role of Tool Calling A major theme throughout the module is: Reasoning vs Action The LLM performs reasoning. The application performs actions. Examples include: • database queries • ERP updates • report generation • sending emails • running calculations Through tool calling, AI becomes capable of interacting with real-world systems. Retrieval Augmented Generation (RAG) Enterprise AI systems often require access to information that was never included during model training. RAG solves this challenge. Question ↓ Document Retrieval ↓ Relevant Context ↓ LLM Reasoning ↓ Answer This enables AI to work with: • accounting policies • contracts • financial statements • internal procedures • audit documentation while reducing hallucinations. Structured Outputs The module explains why enterprise systems require: Structured Outputs rather than unpredictable text. Examples include: • JSON • XML • predefined schemas This allows software systems to reliably process AI-generated outputs. Example: { "customer": "ABC Corporation", "risk_score": 8.4, "action_required": true } Structured outputs are essential for automation. Learned Weights & Inference Engines The session also clarifies key technical concepts. Learned Weights The knowledge stored inside the model. These represent billions of learned relationships developed during training. Inference Engine The runtime environment responsible for: • executing the model • generating responses • serving predictions The inference engine transforms static model weights into useful business outputs. Finance & Accounting Applications These architectural components support: Financial Reporting Agents • retrieve supporting schedules • generate commentary • draft disclosures AP Automation Systems • OCR extraction • vendor validation • workflow routing • ERP integration FP&A Platforms • scenario analysis • forecasting • variance explanations • executive reporting Enterprise Knowledge Systems • policy search • tax research • accounting guidance retrieval • regulatory interpretation 🚀 Why This Matters The future of AI is not: Question → Answer The future is: Question ↓ RAG ↓ Reasoning ↓ Tool Calling ↓ Business Action ↓ Human Review This is the foundation of modern enterprise intelligence systems. DISCLAIMER & LIABILITY NOTICE: The content in this video is for educational and informational purposes only. It does not constitute financial, accounting, tax, or legal advice. No Professional Relationship: Watching this video or interacting in the comments does not create a CPA-Client or fiduciary relationship between you and Sung Lee. Software & Tools: Any code, software, or tools mentioned (including https://www.google.com/search?q=Katchiflow.com) are provided "as-is" for demonstration and drafting purposes only. Outputs should not be relied upon for tax or statutory reporting without independent verification by a qualified professional.
04 How Large Language Models (LLMs) Works? | All about LLMs | What are Tokens & Context Length?
Generative AI | LLM | GenAI | NN | Large Language Models ⏰ Scheduled to be Public from Members Only on 01st Jun 2026 16:00 HRS IST ⏰ ===== In this video, you will learn ===== What is Large Language Model? What is LLM? How LLMs work? Next Token Prediction in LLM, What are Tokens and Context Length? Importance on Tokens in LLM, Different Sampling Controls, LLM Personas and Prompts, Probability Distribution for LLMs ===== Chapters ===== 00:00 - Introduction 00:27 - What are Large Language Models or LLMs? 03:44 - How Large is Large in LLMs? 05:44 - Transformers 08:07 - What are Tokens and their Importance in LLM? 08:18 - What is Vocabulary in LLM? 14:27 - Probability Distribution for Tokens 19:01 - Sampling Controls - Temperature, Top-p 25:51 - Auto-Regressive Generation Loop 28:53 - How LLMs preserves meaning? 30:58 - How LLMs are Trained? 33:15 - What is Fine Tuning? 34:28 - LLM Personas/Roles and Prompts 36:55 - What is Context Length? 39:01 - Model Knowledge Cutoff and Hallucination 41:23 - Open and Closed LLM Models 42:36 - Reasoning Models 43:24 - Multimodal Models ===== Links ===== Google's "Attention is all You Need" Paper - https://arxiv.org/pdf/1706.03762 Groq Cloud - https://console.groq.com/home GPT Tokenizer - https://platform.openai.com/tokenizer ===== Other Playlists ===== Checkout all other playlists on Data Engineering 👇🏻 https://www.youtube.com/@easewithdata/playlists ===== GitHub Repo ===== https://github.com/subhamkharwal ===== Connect with ME ===== LinkedIn - https://www.linkedin.com/in/subhamkharwal Medium - https://subhamkharwal.medium.com ===== Hashtags ==== #genai #dataengineering #python #agenticai #aiagents #aiagent #nn #neuralnetworks
LLMs Explained (10 Minute Masterclass)
@AIwithArunShow Imagine a library the size of Texas containing every word ever written, and a guide who has read it all. In this episode of AI with Arun Show, we peel back the curtain on Large Language Models (LLMs)—the digital wizards behind ChatGPT. We move past the scary jargon to show you how these systems actually function as "autocomplete on steroids," using massive mathematical probability to mirror human thought. Whether you're curious about how AI translates "king - man + woman = queen" or why it sometimes confidently makes things up (hallucinations), this deep dive covers it all. Learn the three pillars of LLMs—Large, Language, and Model—and how to master the "Trust but Verify" rule for effective collaboration. 🚀 Ready to build with AI? Watch now to transition from basic exploration to effective collaboration! 👉 CALL TO ACTION: If you found this breakdown helpful, join our community of AI enthusiasts! Click the JOIN button below to become a member and support the show. 3. Detailed Timestamps 00:00 – The Texas-Sized Library: Visualizing AI’s Knowledge 00:49 – What exactly is an LLM? 01:31 – Breaking down the acronym: Large, Language, Model 02:32 – Autocomplete on Steroids: The Math of Probability 03:26 – The "Once Upon a Time" Game 04:08 – Digital School: How AI is Trained and Coached 06:31 – Embeddings: How Computers Turn Ideas into Algebra 06:53 – From 2017 to Today: The Rise of Transformers 07:28 – The Hallucination Problem: Why AI Makes Things Up 08:42 – 3 Best Practices for Working with AI 09:47 – How to Use Your Creative Partner #AI #ChatGPT #llm #ArtificialIntelligence #MachineLearning #TechExplained #GenerativeAI #DataScience #FutureOfWork #aiwitharunshow Themes How do Large Language Models work simple explanation Understanding the difference between AI and LLMs What are AI hallucinations and why do they happen? The math behind ChatGPT: Embeddings and Probability Training process for AI: Pre-training and Human Coaching "The digital library is officially open! 📚 Which of the three AI best practices—Partnering, Iterative Dialogue, or 'Trust but Verify'—surprised you the most? Drop your thoughts below and tell me what you're planning to build first! 👇" FOR THE COMMUNITY Did you know that AI doesn't actually "understand" ideas, but instead performs complex algebra on concepts? In today’s new video, we’re diving deep into the world of Large Language Models (LLMs). We explore: Why ChatGPT is like an 'imaginative intern' The 'Once Upon a Time' trick that explains AI math How to avoid the trap of AI hallucinations This isn't just about tech jargon; it's about learning how to collaborate with a mirror of our collective human knowledge. Watch the full breakdown here: https://youtu.be/Xi5mLgA9zBI Let's get ahead of the curve together. See you in the comments! 🚀 Made with Google Vids https://vids.new/ #MadeWithGoogleVids
Stanford que Ensina Mais sobre LLMs do que a Maioria dos Profissionais de IA
#llm #stanford #chatgpt #inteligenciaartificial #ia Em vez de assistir a uma hora de Netflix, assista a esta palestra de 2 horas da Stanford que vai te ensinar mais sobre como LLMs como ChatGPT e Claude são construídos do que a maioria das pessoas trabalhando em empresas de IA de ponta aprende em suas carreiras inteiras.Essa é uma das aulas mais valiosas que você vai encontrar sobre o funcionamento real dos grandes modelos de linguagem. Direto, profundo e sem enrolação.Se você quer entender de verdade como essas tecnologias são feitas por dentro, essa palestra é ouro puro. Pare o que está fazendo e invista essas 2 horas. Vale muito mais do que parece.Comente: você prefere usar IA ou entender como ela realmente funciona?
Como o "cérebro" da IA funciona? #inteligenciaartificial #chatgpt #openai
Como a IA moderna funciona? Uma rede neural artificial é um modelo matemático inspirado no funcionamento do cérebro humano. Essa é uma das bases tecnológicas de sistemas modernos de IA, incluindo LLMs como o ChatGPT. #largelanguagemodels #ia #neuralnetworks
Você NÃO entende de IA: vou te mostrar por quê
O que é a inteligência artificial? ChatGPT, Claude, Gemini? Neste vídeo, eu mostro o que essas ferramentas de IA conseguem fazer atualmente na geração de vídeos, músicas, softwares e muito mais. Em seguida, mergulho em um conceito base da IA atual: redes neurais artificiais. Por fim, explico a mecânica de funcionamento de um “large language model” (LLM), ou modelo de linguagem, que é a tecnologia por trás de todos os sistemas modernos de inteligência artificial. Sei que o assunto tá super hypado (ou até saturado), mas acho que hoje é impossível não falar de IA na área de tecnologia. Para o bem e para o mal, essa tecnologia está aí. Tentando entender a fundo como a IA funciona, tive uma sensação de “quebrar a magia”, mas senti também que isso me trouxe tranquilidade no meio de tanto FOMO e medo do desconhecido que esse tema traz junto. 🔗 Plugin para Claude Code citado no vídeo: https://github.com/oprogramadorreal/optimus-claude ⏱️ Capítulos: Eu tô cansado do sensacionalismo! (RANT) - 0:00 O Fim dos Programadores - 1:01 Por que (ainda) precisamos de programadores? - 2:21 O que a IA já consegue fazer - 3:42 O que eu realmente sinto - 4:23 Redes neurais artificiais - 5:54 Large Language Models - 9:05 Próximos passos - 13:12 AI bloopers - 14:32 #InteligenciaArtificial #RedesNeurais #LLM
¿Que son los Large Language Models (LLM)?
#llm #inteligenciaartificial #ia #agentesia En este video veremos una introducción a los Large Language Models (LLM) o Grandes Modelos de Lenguaje. Veremos que son, como funcionan, para que se usan, como se entrenan, que tipos hay y cuales son los principales retos de estos modelos en la actualidad.
LLAMA CPP ⚙️ Domina los Parámetros de Sampleo para un LLM PERFECTO
Descubre cómo funcionan los parámetros de sampleo en los modelos de lenguaje y aprende a configurarlos para optimizar las respuestas de tu IA. En este video analizamos a fondo los ajustes de sampleo, tomando como referencia llama.cpp y LM Studio, aunque estos conceptos aplican a la mayoría de los cargadores de modelos como vLLM. Exploramos la naturaleza de los LLM como predictores de tokens y cómo el contexto influye en la generación de cada palabra. Explicamos detalladamente el impacto de la temperatura en la creatividad y la precisión, la función del Top K para limitar el rango de tokens probables y las diversas penalizaciones por repetición (repeat penalty, presencia y frecuencia) para evitar que el modelo caiga en bucles infinitos. Finalmente, discutimos cómo adaptar estos ajustes según la tarea, ya sea para razonamiento complejo o para la escritura de código. 📝 Índice: 00:00:00 Introducción a los parámetros de sampleo 00:00:51 Funcionamiento del contexto y predicción de tokens 00:03:12 Aleatoriedad vs Probabilidad en las respuestas 00:04:43 La Temperatura y la creatividad del modelo 00:07:31 Top K y la limitación de tokens 00:08:17 Prevención de repeticiones y bucles (Penalty) 00:10:35 Parámetros adicionales en llama.cpp 00:12:00 Configuración según el tipo de uso (Código vs Texto) #inteligenciaartificial #LLM #llamaCPP #LMStudio #MachineLearning #IA #PromptEngineering #Tecnologia Contacto: nichonauta@gmail.com Web: nichonauta.com URL del Directo Completo: https://www.youtube.com/watch?v=WTYkns3r7h8
LLM 🤖 Cómo FUNCIONAN realmente los Modelos de Lenguaje
Descubre cómo funcionan realmente los modelos de lenguaje y desmitifica la idea de que poseen autonomía. En este video exploramos la mecánica técnica detrás de la predicción de texto, el uso de plantillas y cómo los LLM interactúan con herramientas externas. Analizamos el proceso de autocompletado y la estructura de los prompts, diferenciando entre el system prompt, el usuario y el asistente. A través de ejemplos prácticos con llama CPP, se explica que la IA no está teniendo una conversación real, sino calculando la siguiente parte más probable de un texto basándose en fórmulas y pesos matemáticos. También profundizamos en la cadena de razonamiento (reasoning) y el uso de herramientas (tools). Explicamos que cuando un modelo como GitHub Copilot crea un archivo, no está haciendo clic en un botón, sino prediciendo un comando específico que el programa externo luego ejecuta. Finalmente, comparamos el rendimiento de modelos como Gema y Qwen, analizando cómo la cuantización y el tamaño del modelo afectan su capacidad para ejecutar tareas técnicas. 📝 Índice: 00:00:00 ¿Cómo funcionan los modelos de lenguaje? 00:01:12 Estructura de prompts: System, User y Assistant 00:02:30 El proceso de predicción de texto y tokens 00:04:00 Cadenas de razonamiento y etiquetas de pensamiento 00:06:15 El uso de herramientas y comandos externos 00:08:00 Análisis de modelos: Gema, Qwen y cuantización #InteligenciaArtificial #LLM #Programacion #MachineLearning #GithubCopilot #Tecnologia #IA #ModelosDeLenguaje Contacto: nichonauta@gmail.com Web: nichonauta.com URL del Directo Completo: https://www.youtube.com/watch?v=zUUb_rMjzxU
