Generative AI

• Kernel Methods: Algorithms that transform data into higher dimensions to make it more separable. L  • Latent Space: A representation of compressed data capturing underlying features.  • Langchain: A framework for developing applications powered by language models.  • LLM: Large Language Model, trained on vast text data to understand and generate human-like language. M  • Mixture of Experts: A model architecture where different "experts" handle different parts of the input.  • Multimodal AI: AI systems that process and integrate multiple types of data, like text and images. N  • Neural Radiance Fields: A technique for generating 3D scenes from 2D images using neural networks. O  • Objective Function: The function that models aim to optimize during training.  • One-Shot Learning: Learning from a single example to make accurate predictions. Double Tap ❤️ For More

🤖 50+ Generative AI Terms You Should Know A • Agents: Autonomous programs that perform tasks or make decisions on behalf of users. • Attention: A mechanism in neural networks that allows models to focus on relevant parts of the input sequence. • Autoencoders: Neural networks used for unsupervised learning, primarily for dimensionality reduction and feature learning. B • Back Propagation: An algorithm for training neural networks by propagating the error backward to update weights. • BigGAN: A type of Generative Adversarial Network (GAN) designed for high-resolution image generation. • Bias: Systematic errors in AI models due to prejudiced training data or flawed algorithms. C • Capsule Network: A neural network architecture that models hierarchical relationships, improving recognition tasks. • Conditional GAN: A GAN variant where both generator and discriminator receive additional information, enabling controlled generation. • Chain of Thought: A prompting technique that encourages models to reason step-by-step, enhancing problem-solving capabilities. D • DataSpeed: Refers to the rate at which data is processed or transmitted in AI systems. • Double Descent: A phenomenon where increasing model complexity initially leads to overfitting but eventually improves performance. • Diffusion Model: A generative model that learns to reverse a diffusion process, used in image and audio generation. E • Emergent Behavior: Complex patterns arising from simple rules in AI systems, often unexpected. • Expert Systems: AI programs that emulate decision-making abilities of human experts using a set of rules. F • Few-Shot Learning: Models trained to generalize from a small number of examples. • Foundation Model: Large-scale models trained on broad data, adaptable to various tasks (e.g., GPT-4). • Fine-tuning: Adjusting a pre-trained model on a specific task to improve performance. G • Generative AI: AI systems that create new content like text, images, or music. • GPT: Generative Pre-trained Transformer, a type of large language model developed by OpenAI. • GAN: Generative Adversarial Network, consisting of two networks (generator and discriminator) competing to produce realistic data. H • Hyperparameter Tuning: The process of optimizing the parameters that govern the training process of AI models. • Hallucination: When AI models generate plausible but incorrect or nonsensical outputs. • Hidden Layer: Layers in a neural network between input and output layers where computations are performed. I • Image Generation: Creating images from textual descriptions using models like DALL·E. • Instruction Tuning: Training models to follow specific instructions, improving task performance. • Inpainting: Filling in missing parts of images using AI techniques. K • Knowledge Graph: A network of entities and their interrelations, used for information retrieval. • Knowledge Base: A repository of structured information used by AI systems to answer queries.

*P* - *ProGAN*: Progressive GAN, generates images by progressively increasing resolution during training. - *Prompt*: Input given to AI models to elicit a response or perform a task. - *PEFT*: Parameter-Efficient Fine-Tuning, adapting models using fewer parameters. *Q* - *QLoRA*: Quantized Low-Rank Adapter, a technique for efficient fine-tuning of large models. *R* - *Regularization*: Techniques to prevent overfitting by adding constraints to the model. - *RLHF*: Reinforcement Learning with Human Feedback, aligning AI outputs with human preferences. *S* - *StyleGAN*: A GAN variant known for generating high-quality, realistic images. - *Singularity*: A hypothetical point where AI surpasses human intelligence, leading to rapid technological growth. *T* - *Text-to-Speech*: Converting written text into spoken words using AI. - *Transfer Learning*: Applying knowledge from one task to improve learning in another. - *Transformer*: A neural network architecture that uses attention mechanisms, foundational for models like GPT. *React ♥️ for more*

Replace * with ** *🤖 50+ Generative AI Terms You Should Know* *A* - *Agents*: Autonomous programs that perform tasks or make decisions on behalf of users. - *Attention*: A mechanism in neural networks that allows models to focus on relevant parts of the input sequence. - *Autoencoders*: Neural networks used for unsupervised learning, primarily for dimensionality reduction and feature learning. *B* - *Back Propagation*: An algorithm for training neural networks by propagating the error backward to update weights. - *BigGAN*: A type of Generative Adversarial Network (GAN) designed for high-resolution image generation. - *Bias*: Systematic errors in AI models due to prejudiced training data or flawed algorithms. *C* - *Capsule Network*: A neural network architecture that models hierarchical relationships, improving recognition tasks. - *Conditional GAN*: A GAN variant where both generator and discriminator receive additional information, enabling controlled generation. - *Chain of Thought*: A prompting technique that encourages models to reason step-by-step, enhancing problem-solving capabilities. *D* - *DataSpeed*: Refers to the rate at which data is processed or transmitted in AI systems. - *Double Descent*: A phenomenon where increasing model complexity initially leads to overfitting but eventually improves performance. - *Diffusion Model*: A generative model that learns to reverse a diffusion process, used in image and audio generation. *E* - *Emergent Behavior*: Complex patterns arising from simple rules in AI systems, often unexpected. - *Expert Systems*: AI programs that emulate decision-making abilities of human experts using a set of rules. *F* - *Few-Shot Learning*: Models trained to generalize from a small number of examples. - *Foundation Model*: Large-scale models trained on broad data, adaptable to various tasks (e.g., GPT-4). - *Fine-tuning*: Adjusting a pre-trained model on a specific task to improve performance. *G* - *Generative AI*: AI systems that create new content like text, images, or music. - *GPT*: Generative Pre-trained Transformer, a type of large language model developed by OpenAI. - *GAN*: Generative Adversarial Network, consisting of two networks (generator and discriminator) competing to produce realistic data. *H* - *Hyperparameter Tuning*: The process of optimizing the parameters that govern the training process of AI models. - *Hallucination*: When AI models generate plausible but incorrect or nonsensical outputs. - *Hidden Layer*: Layers in a neural network between input and output layers where computations are performed. *I* - *Image Generation*: Creating images from textual descriptions using models like DALL·E. - *Instruction Tuning*: Training models to follow specific instructions, improving task performance. - *Inpainting*: Filling in missing parts of images using AI techniques. *K* - *Knowledge Graph*: A network of entities and their interrelations, used for information retrieval. - *Knowledge Base*: A repository of structured information used by AI systems to answer queries. - *Kernel Methods*: Algorithms that transform data into higher dimensions to make it more separable. *L* - *Latent Space*: A representation of compressed data capturing underlying features. - *Langchain*: A framework for developing applications powered by language models. - *LLM*: Large Language Model, trained on vast text data to understand and generate human-like language. *M* - *Mixture of Experts*: A model architecture where different "experts" handle different parts of the input. - *Multimodal AI*: AI systems that process and integrate multiple types of data, like text and images. *N* - *Neural Radiance Fields*: A technique for generating 3D scenes from 2D images using neural networks. *O* - *Objective Function*: The function that models aim to optimize during training. - *One-Shot Learning*: Learning from a single example to make accurate predictions.

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🧠 10 Graph Algorithms Visualized

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Simplified Process: LLM generates responses → Humans evaluate → Preferred responses identified → Reward signal → Model optimized The goal is to make the model more: Helpful, Safe, Aligned, Instruction-following 11. What is Model Alignment? Model alignment means making an AI system behave consistently with intended human goals, values, and safety requirements. An aligned model should: Follow legitimate instructions, Avoid harmful behavior, Provide useful responses, Respect safety constraints 12. What is Instruction Tuning? Instruction tuning trains a model on examples containing instructions and desired responses. Example: Instruction: "Summarize this article." → Expected Response: "Article summary..." 13. What is Supervised Fine-Tuning (SFT)? Supervised Fine-Tuning trains a model using labeled examples. Example dataset: Instruction → Expected Response: "Translate Hello" → "Bonjour" 14. What is Catastrophic Forgetting? Catastrophic forgetting occurs when a model becomes better at a new task but loses some of its previous capabilities. General LLM → Heavy Domain Fine-Tuning → Excellent domain performance → Reduced performance on some general tasks 15. What are the Risks of Fine-Tuning? Overfitting, Bias amplification, Catastrophic forgetting, Poor-quality outputs, Data leakage, Privacy problems, High training costs 16. How do you prepare data for fine-tuning? Raw Data → Cleaning → Deduplication → Filtering → Formatting → Train / Validation Split → Fine-Tuning Good training data should be: Relevant, Accurate, Diverse, Consistent, High quality 17. How do you evaluate a fine-tuned model? Compare the fine-tuned model against the base model. Evaluate: Accuracy, Task completion, Response quality, Hallucination rate, Safety, Human preference, Domain-specific metrics 18. Fine-Tuning vs Prompt Engineering Prompt Engineering: Changes instructions, Fast, Low cost, No training dataset required, Easy to iterate Fine-Tuning: Changes model parameters, Takes training time, Higher cost, Requires training data, Good for specialized behavior 19. Fine-Tuning vs RAG Use RAG when: Knowledge changes frequently, You need private documents, You need citations/grounding, You want to update knowledge without retraining Use Fine-Tuning when: You need consistent behavior, You need a specific output style, You need task specialization You can also combine them: Fine-Tuned LLM + RAG → Specialized + Grounded AI System 20. Interview Question: Design a Fine-Tuning Strategy Strong Answer: "First, I would establish a baseline using the pretrained model and prompting. Then I would collect and clean high-quality domain-specific data, create train/validation/test splits, and determine whether full fine-tuning or PEFT such as LoRA is appropriate. I would fine-tune the model, evaluate it against the baseline, test for hallucinations and safety issues, and then deploy it with monitoring." 🎯 Key Interview Takeaways Remember these five concepts: Pretraining → General knowledge Fine-Tuning → Specialized behavior RAG → External/updated knowledge LoRA/PEFT → Efficient model adaptation RLHF → Human preference and alignment These distinctions are extremely important in GenAI interviews. Double Tap ❤️ For More ----- 2.26 ₽ · /balance_help

🚀 Generative AI Fundamentals – Part 6 ⚙️ Fine-Tuning, LoRA, PEFT, RLHF & Model Alignment Fine-tuning and model adaptation are important topics for GenAI Engineer, LLM Engineer, and Applied AI interviews. 1. What is Fine-Tuning? Fine-tuning is the process of taking a pretrained model and training it further on a smaller, specialized dataset. Example: General LLM → Financial Documents → Fine-Tuning → Financial AI Assistant The goal is to make the model perform better on a specific task or domain. 2. Pretraining vs Fine-Tuning Pretraining: Initial model training, Very large dataset, Learns general patterns, Expensive, Creates foundation model Fine-Tuning: Additional training, Smaller specialized dataset, Learns specific behavior, Relatively cheaper, Adapts foundation model Simple Example: Pretraining: Learn general English. Fine-tuning: Learn how to answer banking customer-support questions. 3. When Should You Fine-Tune an LLM? Fine-tuning can be useful when you need: ✅ Consistent output format ✅ Specific writing style ✅ Domain-specific behavior ✅ Specialized classification ✅ Task-specific performance ✅ Consistent instruction following Example: A company wants every support response to follow a specific format. Fine-tuning may be more appropriate than repeatedly putting the same style instructions into prompts. 4. When Should You NOT Fine-Tune? Fine-tuning isn't always the best solution. Avoid fine-tuning when the main problem is changing knowledge. Example: A company has thousands of frequently changing policies. Instead of continuously fine-tuning the model, use: RAG → Retrieve the latest policy → Generate answer Rule: RAG changes the information available to the model; fine-tuning changes how the model behaves. 5. What is Parameter-Efficient Fine-Tuning (PEFT)? PEFT allows you to adapt a large model without updating all of its parameters. Large Frozen Model + Small Trainable Parameters → Adapted Model Benefits: Lower GPU requirements, Lower training cost, Faster training, Smaller adaptation files 6. What is LoRA? LoRA stands for Low-Rank Adaptation. It is a popular PEFT technique that freezes the original model weights and adds small trainable matrices. Original Model → Frozen + LoRA Adapters → Fine-Tuned Model 7. What are LoRA Adapters? LoRA adapters contain the learned changes needed for a particular task. Base Model → Finance Adapter, Medical Adapter, Coding Adapter The same base model can therefore be adapted for different applications. 8. What is QLoRA? QLoRA combines: Quantization + LoRA The base model is loaded using lower-precision representations while LoRA adapters are trained. Benefits: Lower memory requirements, Lower hardware cost, Makes large-model fine-tuning possible on more limited hardware 9. What is Transfer Learning? Transfer learning means taking knowledge learned from one task and applying it to another related task. General Language Model → Transfer Learning → Legal Document Model 10. What is RLHF? RLHF stands for Reinforcement Learning from Human Feedback. It uses human preferences to improve model behavior.

9. Components of a RAG System A production RAG system usually includes: Data Source Document Loader Text Splitter Embedding Model Vector Database Retriever LLM Response Generator Each component plays a role in retrieving and generating accurate responses. 10. Advantages of RAG Reduces hallucinations Uses the latest information Supports private enterprise data No need to retrain the model frequently Lower cost than fine-tuning for changing knowledge Improves response accuracy 11. Challenges in RAG Poor document chunking Low-quality embeddings Irrelevant retrieval results Slow retrieval Large context windows Duplicate information Outdated documents Optimizing retrieval quality is often as important as choosing the right LLM. 12. RAG vs Fine-Tuning RAG: Retrieves external knowledge Best for frequently changing data No model retraining Easier to update knowledge Reduces hallucinations with grounded context Fine-Tuning: Updates model behavior Best for specialized tasks Requires additional training More expensive to maintain Improves task-specific performance Rule of Thumb: Use RAG when knowledge changes frequently. Use Fine-Tuning when you need the model to adopt a specific style, behavior, or domain expertise. 13. Common Interview Questions What are embeddings? Why are embeddings important? What is a vector database? What is semantic search? How does similarity search work? What is RAG? Explain the RAG architecture. What are the components of a RAG pipeline? What are the challenges in RAG? RAG vs Fine-Tuning? 🎯 Interview Tip When explaining RAG, use this simple flow: Documents ↓ Chunking ↓ Embeddings ↓ Vector Database ↓ Retriever ↓ LLM ↓ Final Response This end-to-end pipeline is one of the most frequently discussed architectures in GenAI interviews and demonstrates a strong understanding of enterprise AI systems. ➡️ Double Tap ❤️ For More

🚀 Generative AI Fundamentals – Part 5 🔎 Embeddings, Vector Databases, Semantic Search & RAG Deep Dive These concepts are the backbone of modern enterprise GenAI applications. Most LLM Engineer and GenAI interviews include questions on them. 1. Why do LLMs need external knowledge? LLMs are trained on historical data and have limitations: Knowledge becomes outdated Cannot access private company documents by default May hallucinate Cannot answer questions about new information unless connected to external data Example: If a company's HR policy changes today, the LLM won't know it unless it retrieves the latest document. This is why RAG (Retrieval-Augmented Generation) is widely used. 2. What are Embeddings? Embeddings are numerical vector representations of text that capture semantic meaning. Instead of storing text directly, AI converts it into vectors. Example Cat → [0.32, 0.45, 0.87...] Dog → [0.31, 0.47, 0.85...] Car → [0.91, 0.12, 0.44...] Notice that Cat and Dog have similar vectors because their meanings are related. 3. Why are Embeddings Important? Embeddings allow AI to understand meaning, not just exact words. Applications: Semantic Search Recommendation Systems RAG Duplicate Detection Document Clustering Similarity Search 4. What is a Vector Database? A Vector Database stores embeddings instead of plain text. It enables fast similarity searches across millions of vectors. Popular Vector Databases: Pinecone Chroma Weaviate FAISS Milvus Qdrant These databases are optimized for vector similarity search rather than traditional SQL queries. 5. Traditional Search vs Semantic Search Traditional Search: Matches keywords Exact words required Limited context Less accurate Semantic Search: Matches meaning Understands intent Context-aware More relevant results Example Search: "How to lose weight" Semantic search may also return: Fat loss tips Weight reduction strategies Healthy diet plans Even if the exact words don't match. 6. What is Vector Similarity Search? Vector similarity search finds documents whose embeddings are closest to the query embedding. Workflow User Query ↓ Generate Query Embedding ↓ Compare with Stored Embeddings ↓ Find Most Similar Documents ↓ Return Results Common similarity metrics: Cosine Similarity Euclidean Distance Dot Product 7. What is RAG (Retrieval-Augmented Generation)? RAG combines: Information Retrieval Large Language Models Instead of relying only on the model's memory, RAG retrieves relevant information before generating an answer. 8. How does a RAG pipeline work? User Question ↓ Embedding Model ↓ Vector Database ↓ Similarity Search ↓ Relevant Documents ↓ LLM ↓ Final Answer Example: Question: "What is our company's leave policy?" The system: 1. Retrieves the HR policy document. 2. Sends the relevant section to the LLM. 3. Generates an accurate answer based on that document. **9.

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Benefits: Improves diversity Reduces repetitive outputs Balances creativity and quality Top-p is often tuned together with temperature. 10. What is Max Tokens? Max Tokens defines the maximum number of tokens the model is allowed to generate in its response. Example: Max Tokens = 100 The response stops after generating up to 100 output tokens, even if the answer could be longer. This helps control: Response length Latency Cost 11. What is Latency? Latency is the time taken by the model to generate a response after receiving a request. Factors affecting latency: Model size Prompt length Context window Hardware Network Retrieval time (for RAG) 12. What is Inference Cost? Inference cost is the cost of running an LLM for generating responses. It depends on: Number of input tokens Number of output tokens Model size Number of API requests Reducing unnecessary tokens and optimizing prompts can significantly lower costs. 13. Common LLM Interview Questions What is an LLM? How are LLMs trained? What is pretraining? What is fine-tuning? What is RLHF? What are tokens? What are parameters? What is inference? What is a context window? What is temperature? What is top-p sampling? What is inference cost? 🎯 Interview Tip For LLM questions, use this simple structure: 1. Define the concept. 2. Explain how it works. 3. Give a practical example. 4. Mention a real-world use case. 5. Highlight benefits and limitations. This approach makes your answers clear, structured, and interview-ready. ➡️ Double Tap ❤️ For More ----- 2.05 ₽ · /balance_help

🚀 Generative AI Fundamentals – Part 2 🧠 Large Language Models (LLMs) Deep Dive Understanding LLMs is one of the most important topics in GenAI interviews. 1. What is a Large Language Model (LLM)? A Large Language Model (LLM) is a deep learning model trained on massive amounts of text data to understand, generate, summarize, translate, and reason about human language. LLMs are built using the Transformer architecture and predict the next token based on the context of previous tokens. Examples: GPT Llama ChatGPT Claude Mistral 2. How are LLMs trained? LLMs are typically trained in three stages: Stage 1: Pretraining The model learns language patterns from billions of words collected from books, websites, articles, and code. The model learns: Grammar Facts Reasoning patterns Writing styles Relationships between words Stage 2: Fine-Tuning The pretrained model is further trained on domain-specific data. Examples: Medical chatbot Banking assistant Legal assistant Coding assistant This makes the model specialized for particular tasks. Stage 3: Alignment (RLHF) The model learns from human feedback. Goals: Produce safer responses Follow instructions better Reduce harmful outputs Improve helpfulness 3. How does an LLM generate text? User Prompt ↓ Tokenization ↓ Embeddings ↓ Transformer Layers ↓ Attention Mechanism ↓ Probability Distribution ↓ Next Token Prediction ↓ Repeat Until Complete The model predicts one token at a time until the response is finished. 4. What are Tokens? A token is the smallest unit processed by an LLM. Example: Sentence: Artificial Intelligence is amazing. Possible tokens: Artificial Intelligence is amazing . Some tokenizers split words into smaller subwords. Example: unbelievable ↓ un believ able 5. What are Parameters? Parameters are the learned weights inside a neural network. They store everything the model learns during training. Examples: Small model → Millions of parameters Large model → Billions of parameters Generally: More parameters → Better learning capacity More parameters → Higher memory and compute requirements 6. What is Context Window? The context window is the maximum amount of information (measured in tokens) the model can process in one request. It includes: User prompt Previous conversation Retrieved documents System instructions A larger context window helps with: Long documents Multi-turn conversations Better RAG performance 7. What is Inference? Inference is the process of using a trained model to generate predictions or responses. Example: Training → Teaching the model Inference → Using the trained model to answer questions Inference happens every time you interact with an AI chatbot. 8. What is Temperature? Temperature controls the randomness of the generated response. Low Temperature (0.1–0.3) More deterministic Better for factual tasks Less creative High Temperature (0.8–1.2) More creative More varied responses Higher chance of unexpected outputs 9. What is Top-p Sampling? Top-p (nucleus sampling) selects the next token from the smallest set of tokens whose cumulative probability exceeds a chosen threshold.

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Example: Cat → [0.34, 0.67, 0.11...] Dog → [0.32, 0.69, 0.15...] Car → [0.91, 0.18, 0.76...] Cat and Dog embeddings are closer than Cat and Car because their meanings are more similar. Used in: RAG, Semantic Search, Recommendation Systems, Vector Databases 11. Why is Generative AI so Powerful? Because it combines: Massive datasets Powerful GPUs Transformer architecture Large-scale pretraining Cloud computing Advanced optimization techniques 12. Challenges of Generative AI Hallucinations Bias High inference cost Privacy concerns Copyright issues Prompt injection attacks Security risks 13. Common Interview Questions What is Generative AI? How is it different from Machine Learning? What is a Foundation Model? What is an LLM? How does an LLM generate text? What is Tokenization? What are Embeddings? What are the limitations of Generative AI? What are the applications of Generative AI? Why is Generative AI important today? 🎯 Interview Tips When answering Generative AI fundamentals, follow this structure: 1. Define the concept clearly. 2. Explain how it works. 3. Give a real-world example. 4. Mention practical applications. 5. Discuss advantages and limitations. ➡️ Double Tap ❤️ For More ----- 2.08 ₽ · /balance_help

🚀 Generative AI Fundamentals You Should Know 1. What is Generative AI? Generative AI is a branch of Artificial Intelligence that creates new content by learning patterns from existing data. Unlike traditional AI, which mainly predicts or classifies, Generative AI produces original outputs. It can generate: Text Images Audio Video Code Music 3D models Example Input: "Write a Python function to sort a list." Output: The AI generates Python code. 2. Traditional AI vs Generative AI Traditional AI: Predicts outcomes Generative AI: Creates new content Traditional AI: Classification Generative AI: Generation Traditional AI: Fraud detection Generative AI: ChatGPT Traditional AI: Spam filtering Generative AI: AI Image Generation Traditional AI: Recommendation systems Generative AI: AI Code Generation 3. Evolution of AI Artificial Intelligence → Machine Learning → Deep Learning → Foundation Models → Generative AI → Large Language Models Generative AI is built on Machine Learning and Deep Learning. 4. Real-World Applications Healthcare • Medical report generation • Drug discovery • Medical chatbots Finance • Risk analysis • Report generation • Fraud investigation Software Development • Code generation • Bug fixing • Documentation Education • AI tutors • Quiz generation • Content summarization Marketing • Advertisement copy • Social media posts • Product descriptions Customer Support • AI Chatbots • Ticket summarization • FAQ automation 5. Types of Generative AI Text Generation Example: ChatGPT, Claude, ChatGPT Image Generation Example: DALL·E, Midjourney, Stable Diffusion Audio Generation Example: Speech synthesis, AI voice cloning Video Generation Example: AI video creation, Talking avatars Code Generation Example: GitHub Copilot, AI coding assistants 6. What are Foundation Models? Foundation Models are very large pretrained models trained on enormous datasets. Characteristics: • General-purpose • Can perform many tasks • Fine-tunable • Support multiple applications Examples: GPT, Llama, ChatGPT, Claude 7. What is an LLM? LLM stands for Large Language Model. An LLM is trained on billions of words to understand and generate human language. Capabilities: Question Answering, Translation, Summarization, Coding, Reasoning, Text Generation Examples: GPT-4, Llama, Claude, ChatGPT 8. How does an LLM work? Basic workflow: User Prompt ↓ Tokenization ↓ Transformer Model ↓ Probability Prediction ↓ Generated Tokens ↓ Final Response The model predicts one token at a time until the response is complete. 9. What is Tokenization? Tokenization converts text into smaller units called tokens. Example: Sentence: "Generative AI is amazing" Possible Tokens: ["Generative"] ["AI"] ["is"] ["amazing"] The model processes tokens instead of raw text. 10. What are Embeddings? Embeddings convert text into numerical vectors that represent semantic meaning.

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