LLMs manage long inputs using positional embeddings and windowed attention mechanisms. Advanced models extend context windows through efficient attention patterns or external memory modules.
| Generative Models |
Discriminative Models |
| Generate new data samples |
Classify data into categories |
| Examples: GPT, GANs |
Examples: BERT, Logistic Regression |
2️⃣1️⃣ What is a Language Model?
A language model predicts the likelihood of a sequence of words, making it the foundation for many NLP and generative AI applications like chatbots, translators, and content creators.
2️⃣2️⃣ What is Latent Space in AI?
Latent space refers to a compressed, abstract representation of data learned by AI models. It helps models understand relationships between features, often used in autoencoders and generative models.
2️⃣3️⃣ What is Overfitting in AI Models?
Overfitting happens when a model learns the training data too well, including noise and irrelevant patterns, resulting in poor performance on unseen data.
2️⃣4️⃣ What are Embeddings?
Embeddings are dense vector representations of data (like words or sentences) that capture semantic meaning and relationships, enabling AI models to process language more effectively.
2️⃣5️⃣ How is Generative AI Used in Healthcare?
- Synthetic Data Generation
- Medical Imaging Analysis
- Automated Report Writing
- Drug Discovery Simulations
- Virtual Health Assistants
2️⃣6️⃣ What is the Role of GANs in Generative AI?
Generative Adversarial Networks (GANs) consist of two neural networks—a generator and a discriminator—that compete to create realistic synthetic data, especially used in image synthesis and enhancement.
2️⃣7️⃣ How is Generative AI Applied in Finance?
- Risk Modeling and Analysis
- Fraud Detection through Anomaly Patterns
- Synthetic Market Simulations
- Automated Report Generation
- Customer Support Chatbots
2️⃣8️⃣ What is a Diffusion Model?
A diffusion model is a generative process that learns to reverse a gradual noise addition process to generate high-quality data samples, commonly used in AI-driven image synthesis.
2️⃣9️⃣ What is an Autoregressive Model?
An autoregressive model generates sequences where each output token is predicted based on previous tokens, making it effective for tasks like text generation and time series forecasting.
3️⃣0️⃣ What is Temperature in Text Generation?
Temperature is a parameter that controls the randomness of AI-generated outputs. Lower temperatures (e.g., 0.2) make responses more focused and deterministic, while higher temperatures (e.g., 0.8) encourage creativity and diversity.
3️⃣1️⃣ What is Top-k Sampling in Generative AI?
Top-k sampling restricts the AI's output to the top k most probable next tokens, ensuring the model selects from a narrower, high-confidence range, thus controlling randomness while maintaining diversity.
3️⃣2️⃣ What is Top-p (Nucleus) Sampling?
Top-p sampling selects tokens from the smallest possible set whose cumulative probability exceeds p (e.g., 0.9). This dynamic approach allows for more flexible and context-sensitive text generation than top-k sampling.
3️⃣3️⃣ Role of Generative AI in Creative Industries?
- Automated Content Creation (Articles, Scripts)
- Music Composition and Lyric Generation
- AI-Assisted Graphic Design and Art
- Video Editing and Animation
- Interactive Storytelling Experiences
3️⃣4️⃣ Use Cases of Generative AI in Marketing?
- Ad Copy and Social Media Post Generation
- Customer Segmentation Analysis
- Email Campaign Automation
- Personalized Product Descriptions
- Chatbots for Customer Engagement
3️⃣5️⃣ How Does Generative AI Impact Education?
- AI-Powered Tutoring Systems
- Automated Essay Feedback and Scoring
- Quiz and Content Generation
- Adaptive Learning Platforms
- Language Translation Assistance
3️⃣6️⃣ What is Chain-of-Thought Reasoning?
Chain-of-thought reasoning involves AI models generating step-by-step intermediate reasoning paths to arrive at a final answer, improving accuracy in complex problem-solving tasks like math and logic puzzles.
3️⃣7️⃣ What is Multi-modal Generative AI?
Multi-modal Generative AI processes and generates outputs across multiple data types—such as text, images, audio, and video—enabling richer, context-aware AI experiences that mimic human-like perception and creativity.
3️⃣8️⃣ How Does Generative AI Assist in Code Generation?
- Automating Boilerplate Code Writing
- Code Completion and Suggestions
- Refactoring and Optimization
- Bug Detection and Fix Recommendations
- Natural Language to Code Translation
3️⃣9️⃣ What are Token Limits in LLMs?
Token limits define the maximum number of tokens (words/subwords) an LLM can process in a single input or output. Exceeding these limits can result in truncated outputs or processing failures.
4️⃣0️⃣ What is Model Hallucination?
Model hallucination refers to situations where AI generates incorrect or fabricated information that sounds plausible but lacks a factual basis. Reducing hallucinations is a key challenge in AI alignment and safety.
4️⃣1️⃣ How is Generative AI Regulated Globally?
Generative AI regulations vary by region. Initiatives like the EU AI Act, US AI Bill of Rights, India's AI Ethics Framework, and China's AI content guidelines aim to ensure responsible and ethical AI deployment.
4️⃣2️⃣ What is Parameter Tuning?
Parameter tuning involves adjusting model hyperparameters such as learning rate, batch size, number of layers, and temperature to optimize the AI model's performance for specific tasks.
4️⃣3️⃣ What is LoRA (Low-Rank Adaptation)?
LoRA is a fine-tuning technique for Large Language Models (LLMs) that focuses on training a small subset of parameters, reducing memory and compute requirements while maintaining performance efficiency.
4️⃣4️⃣ How is Generative AI Integrated into Mobile Apps?
- On-device Inference using Optimized Models
- Cloud-based API Integrations
- Edge AI for Real-time Processing
- Voice Assistants and Chatbots
- Generative AI-powered Image and Video Editing
4️⃣5️⃣ What is Context Window in LLMs?
A context window defines the maximum number of tokens an LLM can process at once. For instance, GPT-4 can handle up to 128,000 tokens in its extended version, allowing for longer and more coherent outputs.
4️⃣6️⃣ What is the Role of Generative AI in Cybersecurity?
- Threat Modeling and Simulation
- Anomaly and Intrusion Detection
- Automated Incident Response
- Phishing Content Generation for Training
- Vulnerability Report Automation
4️⃣7️⃣ How do AI Models Ensure Factual Consistency?
Techniques like Retrieval-Augmented Generation (RAG), grounding responses in verified data sources, and Reinforcement Learning from Human Feedback (RLHF) help models maintain factual accuracy and reduce hallucinations.
4️⃣8️⃣ What is Prompt Chaining?
Prompt chaining is a method where multiple prompts are linked in a sequence, with each step refining or building upon previous outputs, enabling complex multi-step tasks and workflows in AI applications.
4️⃣9️⃣ What is the Impact of Generative AI on Jobs?
- Automation of Repetitive Tasks
- Creation of New Roles (AI Trainers, Prompt Engineers)
- Upskilling Requirements in AI Supervision
- Job Displacement in Routine Content Creation
- Augmentation of Creative and Analytical Roles
5️⃣0️⃣ What are Some Future Trends in Generative AI?
- Agentic AI Systems (Autonomous Agents)
- Domain-Specific Large Language Models
- Energy-Efficient Training Techniques
- Increased Use of Synthetic Data
- Stricter Global AI Governance Policies
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