Company Interview Questions

Transformers use self-attention mechanisms to process entire sequences in parallel, unlike RNNs which process sequentially. Key advantages: (1) Parallel processing enables faster training, (2) Self-attention captures long-range dependencies better than RNN hidden states, (3) No vanishing gradient problem, (4) Position embeddings encode sequence order. The attention mechanism computes Q, K, V matrices and uses scaled dot-product attention: Attention(Q,K,V) = softmax(QK^T/√d_k)V. This allows the model to "attend" to relevant parts of the input regardless of distance.

Multiple strategies: (1) Retrieval-Augmented Generation (RAG) - ground responses in retrieved facts, (2) Confidence scoring - use model's output probabilities to flag uncertain responses, (3) Consistency checks - generate multiple responses and check for agreement, (4) External verification - cross-reference with knowledge bases or APIs, (5) Fine-tuning on curated, factual data, (6) Prompt engineering - explicit instructions to cite sources or admit uncertainty, (7) Human-in-the-loop validation for critical applications. For production systems, combine multiple approaches and implement monitoring to track hallucination rates.

Reinforcement Learning from Human Feedback (RLHF) is a three-step process: (1) Supervised Fine-Tuning - train base model on high-quality demonstrations, (2) Reward Model Training - collect human preferences on model outputs (A vs B) and train a reward model to predict human preferences, (3) RL Optimization - use PPO (Proximal Policy Optimization) to fine-tune the LLM to maximize the reward model's score. This aligns the model with human values, making it more helpful, harmless, and honest. RLHF is crucial for ChatGPT's quality.

Key techniques: (1) Few-shot prompting - provide examples in the prompt, (2) Chain-of-thought - ask model to "think step by step", (3) Role prompting - assign a specific persona/expertise, (4) System messages - set behavior guidelines, (5) Temperature tuning - lower for factual tasks (0.2), higher for creative (0.8), (6) Constraint specification - be explicit about format, length, style, (7) Negative prompting - specify what NOT to do, (8) Iterative refinement - break complex tasks into steps. Example: "You are an expert Python developer. Write a function to... Explain your approach step by step."

Multiple evaluation approaches: (1) Automatic metrics - BLEU, ROUGE, METEOR for translation/summarization; Perplexity for language modeling, (2) Model-based evaluation - use GPT-4 to grade outputs on helpfulness, accuracy, coherence, (3) Human evaluation - hire raters to score on quality dimensions, (4) Task-specific metrics - accuracy for QA, F1 for NER, (5) A/B testing - compare models in production, (6) Behavioral tests - check for biases, toxicity, factual errors. Best practice: combine automatic metrics for efficiency with human eval for quality assurance. Track metrics over time and across different input distributions.

System design: (1) Data Collection - gather customer support transcripts, FAQs, resolved tickets (need 500+ quality examples), (2) Data Preprocessing - format as prompt-completion pairs, clean PII, balance topics, (3) Fine-tuning Setup - use OpenAI API fine-tuning endpoint, set hyperparameters (learning rate, epochs, batch size), (4) Training - monitor training loss, validation metrics, (5) Evaluation - test on held-out support queries, measure accuracy, helpfulness, safety, (6) Deployment - A/B test against base model, implement fallback to human agents, (7) Monitoring - track user satisfaction, escalation rates, edge cases, (8) Iteration - regularly update with new examples. Consider using RAG with company knowledge base for up-to-date information.