Generative AI - ИИ и ChatGPT

🧠 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