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✅ Top Generative AI Interview Questions with Answers: Part-1 🧠 1. What is Generative AI? Generative AI refers to AI systems capable of creating new content — text, images, audio, code, or video — that resembles human-created output. It learns patterns from data and uses them to generate realistic outputs. 2. Difference between Generative AI and Traditional AI • Traditional AI: Focuses on classification, prediction, or detection (e.g., spam filters). • Generative AI: Creates new content (e.g., writing emails, generating images). Traditional AI uses structured logic, while Generative AI employs neural networks and probability. 3. What are Large Language Models (LLMs)? LLMs are deep learning models trained on massive text datasets to understand and generate human-like language. Examples include GPT-4, Claude, and LLaMA. They use transformer architecture and can perform tasks like summarization, coding, translation, and QA. 4. What is the role of transformers in Generative AI? Transformers are the backbone of most modern generative models. They utilize self-attention mechanisms to understand the relationships between words/tokens, enabling better context understanding and output generation. 5. What is the architecture of GPT? GPT (Generative Pretrained Transformer) uses a *decoder-only* transformer architecture. It includes: • Input embeddings • Positional encoding • Multiple transformer blocks (self-attention + feed-forward layers) • Output layer for next-token prediction 6. How does a language model generate text? It predicts the next word/token based on the input context. Using probabilities, it selects likely words, building the sentence token by token. Techniques like sampling or greedy decoding are used to choose tokens. 7. What is prompt engineering? Prompt engineering involves crafting effective inputs to guide the AI toward desired outputs. It includes techniques like zero-shot, few-shot, and chain-of-thought prompting to improve model responses. 8. What is fine-tuning in LLMs? Fine-tuning involves training a pre-trained model further on task-specific or domain-specific data to enhance performance for a specific use case (e.g., legal, medical, or customer support chatbots). 9. What is tokenization in NLP? Tokenization splits input text into units (tokens), such as words, subwords, or characters. LLMs process input in token form. For example, “ChatGPT” might be split into “Chat” and “GPT” depending on the tokenizer. 10. Difference between pre-training and fine-tuning • Pre-training: Training the model on large general datasets (e.g., internet text) to learn language. • Fine-tuning: Further training on a narrow dataset for specific tasks or domains. Pre-training teaches general knowledge; fine-tuning makes the model task-aware. 💬 Double Tap ♥️ For Part-2!

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Domain-specific model MetalGPT-1 fed metallurgy and mining Data 🟢 What so special?

Trained on Technological protocols, regulations, R&D, construction and design documentation are not texts in the usual ML sense. These are formalized fragments of the production world: the language of processes, chains, constraints, risks. By training an LLM on such a corpus, the company is effectively creating a separate “data-reality layer” that universal models simply do not see.

Domain-first LLMs will become infrastructure. Next will be models for chemical engineering, logistics, energy, construction Each industry has its own language, its own dataset, its own reality. Huggingface #LLM #ML ••••••••••••••••••••••••••••••••••••••••••••••••••••

Advanced Prompt for Coders

“Refactor and improve my code with explanations.”

Use this command:

    
        Analyze the code below and refactor it to be cleaner, faster, and easier to maintain.
Explain your reasoning in natural language, highlight inefficiencies, and describe how the new version improves structure, readability, and performance.
Suggest alternative patterns or architectures that could work better for long-term scalability.
Here is the code: [paste it]{}
    

#AIPrompts #WorkSmarter #AIWorkflow

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✅ Generative AI Roadmap for Beginners (2025) 🤖🧠 1. Understand What Generative AI Is ⦁ AI that creates new content like text, images, or code from patterns in data ⦁ Differs from traditional AI: Focuses on generation (e.g., ChatGPT for text, DALL-E for images) 2. Learn Programming Basics ⦁ Start with Python—essential for AI with libraries like NumPy and Pandas ⦁ Cover variables, loops, functions; use free tools like Google Colab 3. Master Math & Stats Fundamentals ⦁ Linear algebra, calculus, probability ⦁ Key concepts: Vectors, gradients, distributions for model understanding 4. Dive into Machine Learning Basics ⦁ Supervised/unsupervised learning, neural networks ⦁ Tools: Scikit-learn for simple models 5. Explore Deep Learning Concepts ⦁ ANN, forward/backward propagation, activation functions ⦁ Frameworks: TensorFlow or PyTorch for building networks 6. Learn Core Gen AI Models ⦁ GANs (Generative Adversarial Networks) for images ⦁ VAEs (Variational Autoencoders), Transformers for text 7. Practice Prompt Engineering ⦁ Craft effective prompts for LLMs like GPT ⦁ Techniques: Specificity, role-playing to reduce biases 8. Work on Hands-On Projects ⦁ Build a simple text generator or image creator ⦁ Use datasets from Kaggle; integrate APIs like OpenAI 9. Understand Ethics & Applications ⦁ Bias mitigation, hallucinations ⦁ Real-world: Content creation, chatbots, art generation 10. Bonus Skills ⦁ Advanced: RAG (Retrieval-Augmented Generation), fine-tuning models ⦁ Certifications: Google AI Essentials or Coursera Gen AI courses 💬 Double Tap ♥️ For More

Sometimes reality outpaces expectations in the most unexpected ways. While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included. ✅ No API paywalls. ✅ No usage restrictions. ✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs. What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers. GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments. GitHub | HuggingFace | GitVerse GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count. Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support. GitHub | Hugging Face | GitVerse Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation. GitHub | GitVerse | Hugging Face | Technical report Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech. GitHub | HuggingFace | GitVerse Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.

🌐 Generative AI Tools & Their Use Cases 🎨🤖 🔹 ChatGPT ➜ Text generation for content creation, brainstorming, and conversation 🔹 Midjourney ➜ AI image generation from text prompts for art and visuals 🔹 Stable Diffusion ➜ Open-source image synthesis for custom graphics and editing 🔹 DALL-E 3 ➜ High-quality image creation integrated with ChatGPT for design 🔹 Jasper AI ➜ Marketing copy, blog posts, and SEO-optimized content writing 🔹 Synthesia ➜ Video generation with AI avatars for presentations and training 🔹 Runway ML ➜ Video editing, animation, and generative effects for media 🔹 GrammarlyGO ➜ AI writing assistance for tone adjustment and proofreading 🔹 Google Gemini ➜ Multimodal content creation with Google Workspace integration 🔹 Copy.ai ➜ Sales copy, ad scripts, and email campaigns automation 🔹 Notion AI ➜ Note-taking, summarization, and knowledge base enhancement 🔹 GitHub Copilot ➜ Code generation and autocompletion for developers 🔹 Lumen5 ➜ Video creation from text for social media and marketing 🔹 ElevenLabs ➜ Voice synthesis and audio generation for podcasts and dubbing 🔹 Suno ➜ Music composition and song generation from lyrics or prompts 💬 Tap ❤️ if this helped! Midjourney's images are mind-blowing for quick concepts! Which GenAI tool are you experimenting with? 😊

✅ Generative AI Roadmap: Beginner to Advanced 🤖✨ 1️⃣ Basics of AI & ML - Difference: AI vs ML vs Deep Learning - Supervised vs Unsupervised Learning - Common algorithms: Linear Regression, Clustering, Classification 2️⃣ Python for AI - NumPy, Pandas for data handling - Matplotlib, Seaborn for visualization - Scikit-learn for ML models 3️⃣ Deep Learning Essentials - Neural networks basics (perceptron, activation functions) - Forward/backpropagation - Loss functions & optimizers 4️⃣ Libraries for Generative AI - TensorFlow / PyTorch - Hugging Face Transformers - OpenAI’s API 5️⃣ NLP Fundamentals - Tokenization, Lemmatization - Embeddings (Word2Vec, GloVe) - Attention & Transformers 6️⃣ Generative Models - RNN, LSTM, GRU - Transformer architecture - , BERT, T5 overview 7️⃣ Prompt Engineering - Writing effective prompts - Few-shot, zero-shot learning - Prompt tuning 8️⃣ Text Generation Tasks - Text summarization - Translation - Question answering - Chatbots 9️⃣ Image Generation - GANs (DCGAN, StyleGAN) - Diffusion Models (Stable Diffusion) - DALL·E basics 🔟 Audio & Video Generation - Text-to-speech (TTS) - Music generation - Deepfake basics 1️⃣1️⃣ Fine-Tuning Models - Using pre-trained models - Transfer learning - Custom dataset training 1️⃣2️⃣ Tools & Platforms - Google Colab, Jupyter - Hugging Face Hub - LangChain, LlamaIndex (for agents, RAG) 1️⃣3️⃣ Ethics & Safety - Bias in AI - Responsible use - Model hallucination Project Ideas: - AI chatbot - Text-to-image app - Email summarizer - Code generator - Resume analyzer Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U 💬 Tap ❤️ for more!