Michael Iantosca’s Post

Agentic RAG has the potential to make complex products more intuitive for end users by breaking down users' asks into dynamic workflows using reasoning and planning orchestrator, which in turn leverage tools, sessions, and specialized LLM agents. Since natural language is the key interface among agents, it allows the system to dynamically reason, find the right set of tools, and execute them on the user's behalf. We don't need to have big training manuals for the products and steep learning curve for the user. #AgenticRAG #RAG #Agents #LLM

When using a GPT, you quickly encounter that the prompt definition is critical to the quality of the output. Formulating a good prompt is not easy though. That’s where Powl steps in. Powl offers a structured approach to prompt optimization, guiding users to consider essential elements like persona, action, content, format, and context. This tool ensures that each prompt is crafted with intention and clarity, leading to efficient, effective results. Whether you're new to prompt design or a seasoned pro, Powl’s user-friendly framework takes the guesswork out of prompt creation, making it accessible for everyone. Curious to see how it can transform your approach? Try Powl and experience the productivity boost firsthand!

When using a GPT, you quickly encounter that the prompt definition is critical to the quality of the output. Formulating a good prompt is not easy though. That’s where Powl steps in. Powl offers a structured approach to prompt optimization, guiding users to consider essential elements like persona, action, content, format, and context. This tool ensures that each prompt is crafted with intention and clarity, leading to efficient, effective results. Whether you're new to prompt design or a seasoned pro, Powl’s user-friendly framework takes the guesswork out of prompt creation, making it accessible for everyone. Curious to see how it can transform your approach? Try Powl and experience the productivity boost firsthand: https://lnkd.in/es9hNzKw

Gling Gling is an AI tool made for video content creators. Their AI will cut silences and bad takes for you so you can focus on your YouTube videos. Try now 👇 https://lnkd.in/dbjWTz_t #VideoEditing #aitools #victrays #ChatGPTPrompts

Conquered Level 3 of the Gen AI Bootcamp with Pathway × GTech MuLearn! 🌟 I'm thrilled to share some key takeaways from my level 3 experience, where I delved into the fascinating world of word vectors. Imagine words as unique addresses in a high-dimensional space. Word vectors are like GPS coordinates, capturing the meaning and relationships between words using numbers. This allows large language models (LLMs) to understand context, which is crucial not just for technical accuracy but also for real-world applications. LLMs leverage context to perform a variety of tasks, from fixing typos to comprehending complex text. Let's peek inside a generative AI chatbot powered by an LLM. The journey begins with your prompt. The LLM translates this prompt into numerical tokens using a dictionary, establishing the initial context. Then, it iteratively predicts the next word based on the existing context, building a continuous response. Once complete, the generated tokens are transformed back into human-readable language for you to see! Overall, this level deepened my understanding of how AI tackles language, and I'm excited to explore its potential further. For more info, do check out https://lnkd.in/gys8z4T3

Well said Seema. With GenAI and automation leaving agents with the toughest customer problems to solve, leaders are hyper-focused on developing agent skills and judgment, not teaching agents through keywords and memorization. #skills #judgement #learninganddevelopment #topperformers

Embracing AI-powered skill development over mere keyword memorization is a forward-thinking approach that aligns with the rapidly evolving nature of work and technology.

It's crucial to approach AI-generated content with a critical eye and to be aware of its limitations. By understanding the technology's strengths and weaknesses, we can use it effectively while avoiding its pitfalls.

🔍 Exploring Word Vectors and Context in Large Language Models on Level 3 with GTech MuLearn x Pathway 🔍 Ever wondered how language models like GPT-4 understand the meaning of words and their relationships? It's all about word vectors and context! Words are assigned unique numbers and represented in a multidimensional space through vector embeddings. These vectors capture semantic similarities between words, allowing the model to understand how they relate to each other. Understanding the role of context in large language models (LLMs) is crucial for deciphering the complexities of human language. LLMs, like GPT (Generative Pre-trained Transformer) models, rely on context to generate coherent and contextually relevant responses.  Inside the Chatbot Brain: Ever wondered how generative AI chatbots work? Let’s dive in! User Prompt: It all starts with your input. Tokenization: The chatbot converts your words into numeric values using a tokenizer and a dictionary. Context Matters: The tokenized text, along with the raw prompt, enters the chatbot’s context. Token Generation: The chatbot predicts the next token based on context, creating a continuous output. Detokenization: When the model hits its token limit, it signals the app. The output tokens get detokenized back into natural language for you! But it's not just about individual words—it's also about context. Just like GPT-4 uses attention mechanisms to weigh the importance of different words in a sentence, understanding context is crucial for giving the model a deeper understanding of language. To know more about Word vectors check out the link: https://lnkd.in/g9xbFSdr #pathway #mulearn

Level 3 of AI bootcamp conducted by GTech MuLearn & Pathway completed! This incredible program unlocked the fascinating world of Large Language Models (LLMs) and I'm here to share some key takeaways. Imagine a world where machines can understand language as well as humans! Word vectors are the secret sauce that makes this possible. These numerical representations capture the essence of words and their relationships, allowing LLMs to decipher meaning. We even explored the history of word vectors, with Google's Word2Vec project being a major game-changer. But LLMs are more than just fancy vocabulary. Context is crucial for true comprehension. The bootcamp shed light on how LLMs use context to understand the finer points of communication. By analyzing the surrounding words and phrases, LLMs can generate responses that are relevant and on-point. Finally, we tackled the magic of tokenization and detokenization. These processes act like translators, transforming user queries into a language that LLMs can understand and vice versa. It's the invisible bridge between our questions and the informative answers LLMs provide! This bootcamp has sparked my passion for AI and LLMs. I'm excited to see how these powerful tools can revolutionize various industries and shape the future. Here is the link to the repo : https://lnkd.in/gCkFEXwB #AI #LLMs #GTechMuLearn #Pathway #MachineLearning #ArtificialIntelligence #NLP