Build a Chatbot with Python and Rasa

Learn chatbot development with Python and Dialogflow in this step-by-step guide, covering benefits, steps, and common challenges of building a conversational AI model https://lnkd.in/gsQd9z2R #ChatbotDevelopmentWithPython Read the full article https://lnkd.in/gsQd9z2R

Someone asked the McDonald's support chatbot how to reverse a linked list in Python. The bot answered. Then asked if they wanted fries with that. It's kind of a perfect example of what happens when you ship an AI feature with no guardrails on what it should and shouldn't do. The bot was just doing what LLMs do which is answer whatever you ask. Nobody told it at the system level that its job is only burger support. Is it just me or are things like this starting to happen way more often? 😅 #AIGovernance #ResponsibleAI #AI #LLMs Picture from Simon Villani, PhD's linkedin post.

day TASK 1- Built a Role-Based AI Chatbot using Python and Ollama with dynamic prompt engineering and conversation memory. Demonstrates how AI responses change based on different roles. #nunnariacademy #aipoweredprogram #ai #freebootcamp #nunnarilabs #generativeai #freebootcamp2026

Why RAG Matters? AI without context = weak answers ❌ AI with context = powerful system ✅ That’s why RAG is changing everything. With tools like: 📚 LangChain 📊 Pinecone You can build AI that actually understands your data. This is how real AI products are built today. #RAG #LangChain #Pinecone #AIEngineering #Python #LLM #Upwork

⚠️ Fake news spreads faster than real news… but what if we could stop it? Developed a Fake News Detection project using Python & Machine Learning that classifies news articles as True or Fake. 🔧 Behind the scenes: ✔ Data preprocessing & cleaning ✔ Feature extraction using TF-IDF ✔ Model training (ML classification) ✔ Real-time prediction system 📈 This project shows how AI can be used to tackle real-world problems like misinformation. 🌍 A step towards building a more informed and aware society. #AI #MachineLearning #Python #DeepLearning #TechForGood #DataScienc

The Generative AI space can feel overwhelming but the path is simpler than it looks. From Python fundamentals to building scalable GenAI systems, this roadmap breaks it down into actionable steps. The key isn’t learning everything it’s building real, useful systems along the way. Consistency > Complexity. Where are you currently on this roadmap? #GenerativeAI #MachineLearning #DeepLearning #AI #DataScience #LLM #Transformers #RAG #AIEngineering #TechCareers #LearningJourney #Python #Innovation

🚀 Built a Text-to-Image Generator using Python, Streamlit, PyTorch, and Stable Diffusion v1.5 Recently worked on a hands-on GenAI project where I built a simple app that converts text prompts into images. This experience gave me a deeper understanding of how pretrained generative models work in real-world applications — especially how prompt-based image generation actually behaves beyond theory. 💡 One of the most interesting takeaways: After the initial setup and model download, the entire system can run locally — generating images without relying on cloud services or internet. 🔍 Key things I explored: • Local model inference • Prompt → image generation • Integrating AI models into user-facing applications Overall, a great step forward in my journey of exploring Generative AI and building practical use cases. #Python #AI #GenerativeAI #MachineLearning #StableDiffusion #PyTorch #Streamlit #DeepLearning

Units Matter in AI. If you aren’t scaling your features, you’re basically telling your AI models that "cents" are more important than "dollars." Scaling ensures every feature gets a fair vote in the final prediction. I’ve put together a quick visual guide on why this happens and the two main paths to fix it: Normalization and Standardization. 🚀 Part 1: The Theory 🔜 Part 2: Python Implementation (Coming Soon!) Check out the visual breakdown below! 🎥 #DataAnalytics #DataScienceTips #MachineLearningEngineer #TechTips #PythonProgramming #DataVisualization #CareerInTech

AI models don’t work alone. Behind every prediction is logic — conditions, loops, and decision rules that turn outputs into real actions. Understanding this layer is what separates using AI from building it. #AI #MachineLearning #Python #DataScience

Encoder models don’t chat, and they don’t get much attention for it. But they power most of the NLP that runs in production, from embeddings to ranking and classification. Deploying one still often means dragging along a Python runtime, a full dependency tree, and serving infrastructure designed for something much larger. Encoderfile rethinks that. A single executable with an appended payload that includes model weights, tokenizer data, and a manifest. No compile-time embedding, just a format that can be inspected and understood. See the full breakdown: https://lnkd.in/estq-six