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I recently found interest in the field of Generative AI, for which I started learning LangChain one of the most powerful frameworks for building applications with large language models. In my recent learning session on Prompts in LangChain, I discovered how crucial effective prompt design is in shaping the performance and accuracy of AI models. Here are some key takeaways from what I learned: • Prompts are the heart of LLM interactions they determine what you ask and how you ask it, directly influencing the quality and reliability of the output. • There’s a clear difference between static prompts (fixed text) and dynamic prompts (templates with variables). Dynamic prompts make it easier to reuse and adapt logic for different use cases. • Using Prompt Templates (like PromptTemplate in LangChain) helps modularize prompt design, making the workflow more structured and scalable. • Incorporating examples or context (few shot learning) can guide the model’s behavior to produce more accurate and consistent responses. • The structure of a prompt including roles, tone, and constraints can significantly affect how an LLM interprets and responds. This made me realize that prompt engineering is not just a supporting skill it’s foundational to building reliable AI applications. Going forward, I plan to: 1. Refactor prompts in my existing projects into reusable templates 2. Experiment with variable substitution and conditional logic 3. Benchmark different prompt strategies to find what works best. If you’re working with LLMs, investing time in prompt design isn’t optional it’s foundational. Happy to share more or collaborate if you’re exploring this space too!

Artificial intelligence is transforming the way we interact with technology, yet many of us still find ourselves unsure of its potential. Understanding key concepts is essential for anyone looking to thrive in the digital landscape. Start by familiarizing yourself with the basics of AI, including machine learning and natural language processing. Explore online resources and reputable articles to enhance your knowledge. Engaging with these materials can empower you to apply AI solutions effectively in your projects. By taking these steps, you'll not only boost your confidence but also position yourself as a forward-thinking professional in your field. Embrace the opportunities AI brings and watch your skillset expand. What are your thoughts on implementing AI in your work? I'd love to hear your insights or any tips you may have! https://lnkd.in/dpqvWfj7

Learning is always fun. I spent 20+ hours studying Generative AI. Here are 3 ingredients that will turn your AI journey into a valuable skill: 1. Large Language Models (LLMs) The number one thing you need to understand is how LLMs process information. Secondarily, you need to grasp the concept of tokens and context windows. Combine both for better AI interactions and outputs. 2. Prompt Engineering Master the art of crafting clear, specific instructions. The classic example is "write a blog post on AI" vs "write a compelling 500-word blog post about AI trends in 2025." Use system prompts and then iterate your instructions for better results. 3. Custom GPTs AI is evolving rapidly, so building custom solutions is very important. Start by identifying your specific use case and then design your GPT's personality until you achieve your desired outcome. Don't limit yourself to basic prompts but also don't overcomplicate your instructions. Want to learn more about AI? Join me in my journey of exploration and let's build something amazing together.

Tencent AI Lab and University of Maryland researchers have introduced Parallel-R1, a novel reinforcement learning technique. This framework teaches language models "parallel thinking," enabling them to branch into multiple reasoning paths when tackling complex problems. By learning to explore different solutions and then summarize findings, models achieve more robust and accurate conclusions. This approach unlocks greater reasoning power through efficient inference-time scaling, without requiring expensive new training data. It represents a significant step toward more capable and reliable AI systems for complex enterprise applications. #AI #ParallelR1 #LLM #AIResearch #ReasoningAI #TencentAI

The AI PM in the US makes $187K/yr (Glassdoor). But AI isn’t nice to have anymore. It might be a ticket to keeping the job. So, where to start? Step 1: Quickly Get The Basic Terms (no coding) Step 2: Lean by Doing, Not Theorizing (no coding) Step 3: Master AI Product Strategy Let's break it down: 𝗦𝘁𝗲𝗽 𝟭: 𝗚𝗲𝘁 𝗧𝗵𝗲 𝗕𝗮𝘀𝗶𝗰 𝗧𝗲𝗿𝗺𝘀 One of the key concepts is neural networks. You can quickly learn them using the TensorFlow Playground. It's a free, interactive tool that lets you experiment and see how they work in action: https://lnkd.in/dMewUQ2n Next, explore Large Language Models (LLMs) using this interactive visualization: https://bbycroft.net/llm Finally, to understand tokens, try this free OpenAI Tokenizer: https://lnkd.in/dcJCqQuW 𝗦𝘁𝗲𝗽 𝟮: 𝗟𝗲𝗮𝗿𝗻 𝗯𝘆 𝗗𝗼𝗶𝗻𝗴, 𝗡𝗼𝘁 𝗧𝗵𝗲𝗼𝗿𝗶𝘇𝗶𝗻𝗴 Start With Prompt Engineering (free guides): • GPT-5 Prompting Guide (agents): 14 Prompting Techniques for PMs: https://lnkd.in/dnCGVH6a https://lnkd.in/dXkgfASg • GPT-4.1 Prompting Guide: https://lnkd.in/dt8FxriE • Anthropic Prompt Engineering: https://lnkd.in/dc-kucif • Prompt Engineering by Google: https://lnkd.in/dEU2Y_9v • Free course by Google: https://lnkd.in/dTPs2YV9 • Free course by Anthropic: https://lnkd.in/dCJRdSME Continue With Practical Exercises (~60 min each): • Prototype with AI: https://lnkd.in/dPaKYv7S • Build a RAG chatbot (n8n, Pinecone, Lovable): https://lnkd.in/dew--RqD • Build an AI voice agent (ElevenLabs, n8n): https://lnkd.in/dSW6_KiT • Learn MCP by automating Figma > Jira: https://lnkd.in/dx2kesVT • Experiment with AI agents: https://lnkd.in/dnmQ_kP8 • Manage the Context: https://lnkd.in/d_XVN5KV • Experiment with AI evals: https://lnkd.in/d9tZcvVT • Add Observability: https://lnkd.in/ddQTFw6n 𝗦𝘁𝗲𝗽 𝟯: 𝗠𝗮𝘀𝘁𝗲𝗿 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 (and lead) • How to Think About AI Product Strategy: https://lnkd.in/ds9EQ7hS • 5 Phases To Build, Deploy, And Scale Your AI Strategy: https://lnkd.in/dShxwfsi • 3-Layer AI Product Distribution Framework: https://lnkd.in/dejzkwYG • AI PRD: A PRD template by Miqdad Jaffer (OpenAI): https://lnkd.in/dQ2jiMfr Thank you Paweł Huryn for sharing. #ai #cybersecurity #machinelearning #management #technology

What Is Word Embedding in Deep Learning Representing Text as Numbers Machines can't understand language like we do. They need numerical text representation to process text well. This is a big challenge for artificial intelligence today.Old methods like one-hot encoding make vectors that are too sparse and not efficient. They can't really show how words relate to each other. This makes them not very good for complex tasks.Word embeddings change this by making numeri

What Is Word Embedding in Deep Learning Representing Text as Numbers Machines can't understand language like we do. They need numerical text representation to process text well. This is a big challenge for artificial intelligence today.Old methods like one-hot encoding make vectors that are too sparse and not efficient. They can't really show how words relate to each other. This makes them not very good for complex tasks.Word embeddings change this by making numeri

Machine Learning vs Deep Learning: what’s the real difference? Everyone’s talking about AI, but few truly understand how Machine Learning (ML) and Deep Learning (DL) actually differ. Here’s the truth: -All Deep Learning is Machine Learning, but not all Machine Learning is Deep Learning. Let’s break it : 🧠 Machine Learning (ML) Machine Learning is the foundation of modern AI — it teaches systems how to learn from data and make decisions without being explicitly programmed. It includes algorithms like: Decision Trees🌲 Random Forests 🌲 Support Vector Machines (SVM) 📈 Linear/Logistic Regression 📊 When to use it: ML shines when your data is structured (like tables or spreadsheets) and your goal is prediction or classification. 📌 Example: Predicting customer churn, forecasting sales, or filtering spam emails. 🤖 Deep Learning (DL) Deep Learning takes things to the next level — it’s a subset of ML built on neural networks that can automatically learn complex patterns from massive datasets. It’s the engine behind modern AI breakthroughs like: Computer Vision 👁️ Natural Language Processing 💬 Speech Recognition 🎙️ When to use it: DL is ideal when your data is large, complex, or unstructured (images, audio, text). It leverages models like CNNs and RNNs, but demands high computational power (GPUs/TPUs). 📌 Example: Image classification, voice assistants, autonomous driving, or medical image analysis. ✨ Takeaway: Deep Learning is a powerful evolution of Machine Learning — but it’s not always the right tool. Sometimes, a simple ML model can outperform a complex DL model if your data is limited or well-structured. At the end of the day, it’s not about using what’s trendy — it’s about using what’s effective. #MachineLearning #DeepLearning #AI #DataScience #ArtificialIntelligence #Innovation #Technology

It may seem like AI is at its peak hype cycle, but some application areas are just getting started. Large language models (LLMs) stole our attention, but the enabling technology has been incubating for years. Now, the lessons we’ve learned from LLMs are trickling into other areas, leaving them well-poised for their own advancements. Computer vision is one such area. Just as foundation models like GPT set the stage for chatbots and various other language applications, image-based foundation models are enabling a revolution in advanced image analysis, from personalized medicine to precision agriculture to industrial automation. While early LLMs had fewer than 1 billion parameters, today’s GPT, Bard, and LLaMa now have more than one trillion parameters. The largest computer vision models like DINO and Segment Anything top out around 1 billion parameters. They’re not yet as large as LLMs but are heading in that direction. Training such a large model requires an enormous amount of training data. For example, DINOv2 was trained with 142 million images. Using the advancements of self-supervised learning, the training data does not even need to be labeled. Massive amounts of unlabeled data are all that is needed to learn patterns. For general-purpose applications, large training sets and large models are paving the way for new utility. They can be easily adapted for classification, detection, or segmentation tasks on many different types of imagery. In many ways, bigger is better. Read this article to learn more: https://lnkd.in/emqkWf9F #MachineLearning #DeepLearning #ComputerVision