Generative AI Interview Questions for Freshers

An LLM is a transformer-based model trained on massive text corpora to predict the next token. Architecture: decoder-only transformer (GPT family) with billions of parameters. Training: causal language modeling — given tokens 1..n, predict token n+1; trained on trillions of tokens from internet text, books, code. Emergent capabilities appear at scale: reasoning, coding, few-shot learning, instruction following. Inference: autoregressive generation — sample one token at a time, append to context, repeat. Temperature controls randomness (0=greedy, 1=sampling, >1=creative). Context window = max tokens the model can attend to simultaneously.

Prompt engineering crafts input text to elicit desired LLM behavior without changing model weights. Key techniques: (1) Zero-shot: direct instruction — "Classify this review as positive/negative:", (2) Few-shot: provide examples before the task — 2–5 labeled examples improve accuracy significantly, (3) Chain-of-Thought (CoT): "Think step by step" — forces LLM to reason explicitly before answering, improves complex reasoning by 10–40%, (4) Self-consistency: sample multiple CoT paths, take majority vote, (5) Role prompting: "You are an expert Python developer...", (6) Structured output: "Respond only in JSON format: {field: value}". For production: always version and test prompts — they degrade with model updates.

GPT-4 (OpenAI): strongest reasoning, best function calling, industry standard for agentic tasks, expensive, closed-source. Claude (Anthropic): longest context window (200K), best for document analysis, strong at following complex instructions, designed for safety. Gemini (Google): multimodal-first, integrated with Google ecosystem, competitive on reasoning. Llama 3 (Meta): open-source, can self-host, 70B rivals GPT-3.5 class. Mistral/Mixtral: efficient open-source models, MoE architecture. Selection criteria: (1) Task requirements — function calling (GPT-4), long documents (Claude), self-hosting (Llama), (2) Cost — Llama hosted on Groq is 10x cheaper than GPT-4, (3) Latency — Groq inference is fastest, (4) Compliance — self-hosted for data sovereignty.