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Python / Python Modern Generative AI and Agents Interview Questions

What are Large Language Models (LLMs) and how do they generate text?

Large Language Models (LLMs) are neural networks — almost universally transformer-based — trained on massive text corpora to learn the statistical patterns of language. At inference, they generate text autoregressively: given a sequence of input tokens, the model produces a probability distribution over the entire vocabulary for the next token, a token is sampled from that distribution, appended to the sequence, and the process repeats until a stop token or length limit is reached.

This generation process is controlled by several parameters. Temperature scales the logit distribution before softmax — temperature < 1 sharpens the distribution (more deterministic, picks the most likely token more often), temperature > 1 flattens it (more random and creative). Top-k restricts sampling to the k highest-probability tokens; top-p (nucleus sampling) restricts to the smallest set of tokens whose cumulative probability exceeds p. These prevent sampling from extremely low-probability tokens (gibberish) while preserving diversity.

from openai import OpenAI

client = OpenAI()  # reads OPENAI_API_KEY from environment

response = client.chat.completions.create(
    model='gpt-4o',
    messages=[
        {'role': 'system', 'content': 'You are a helpful assistant.'},
        {'role': 'user',   'content': 'Explain transformer attention in one paragraph.'},
    ],
    temperature=0.7,     # creativity knob: 0=deterministic, 2=very random
    top_p=0.95,          # nucleus sampling: sample from top 95% mass
    max_tokens=300,
)

print(response.choices[0].message.content)
print('Tokens used:', response.usage.total_tokens)

Key Generation Parameters
ParameterEffectTypical value
temperatureScales logits before softmax — controls randomness0.0–0.3 factual, 0.7–1.0 creative
top_pNucleus sampling — keeps smallest token set summing to p0.9–0.95
top_kRestricts vocab to k most likely tokens40–100
max_tokensHard limit on output lengthTask-dependent
presence_penaltyDiscourages repeating topics already mentioned0–2
frequency_penaltyDiscourages repeating individual tokens0–2
How does LLM text generation work at each step?
What does a temperature of 0 produce in LLM generation?

More Related questions...

What are Large Language Models (LLMs) and how do they generate text? What is the Hugging Face Transformers pipeline API and how do you use it for common NLP and vision tasks? How does tokenisation work in Hugging Face and what are the key tokenizer concepts? What is the Auto-class pattern in Hugging Face and how do you run inference with a raw model? What is prompt engineering and what are the most effective techniques for getting better outputs from LLMs? What is Retrieval-Augmented Generation (RAG) and why is it preferred over full fine-tuning for knowledge-intensive tasks? What are vector databases and how do they enable semantic search in RAG pipelines? How do you build a complete RAG pipeline using LangChain? What are the most important text splitting strategies in RAG, and how do chunk size and overlap affect retrieval quality? What are LangChain's core abstractions — Chains, Runnables, and the LangChain Expression Language? How do you add conversation memory to an LLM application with LangChain? What is an AI agent and how does function calling / tool use work in LLM-based agents? What is the ReAct agent pattern and how does LangChain implement it? How do you efficiently load large Hugging Face models for inference, including quantization and device placement? How do you use Hugging Face's text-generation pipeline with open-source chat models like Mistral or Llama? How do you use the Hugging Face Inference API and the InferenceClient for production deployments? What is LoRA and how does the Hugging Face PEFT library simplify fine-tuning large models? How do you use the Hugging Face Datasets library for training and evaluation? How do you fine-tune a model using the Hugging Face Trainer API? How do you evaluate LLM outputs for quality, factual accuracy, and hallucination? How do you stream LLM responses token by token for a better user experience? How do you use multimodal models (vision-language) with Hugging Face for image understanding tasks? How do you reliably get structured JSON output from LLMs, and what tools does LangChain provide? How do you compute semantic similarity between texts using Hugging Face and OpenAI embeddings? What document loaders does LangChain provide, and how do you handle different file types in a RAG pipeline? What is the OpenAI Assistants API and how does it differ from the Chat Completions API? What is the Parent Document Retriever pattern and when does it improve RAG performance? How do you manage, version, and reuse prompts in production LLM applications? How do you generate and manipulate images using Hugging Face's Diffusers library? How do you handle documents or conversations that exceed an LLM's context window? What is LangGraph and how does it differ from LangChain's AgentExecutor for building agents? What embedding models should you use for production RAG systems, and how do you choose between OpenAI and open-source options? How do you add safety guardrails and input/output validation to LLM applications? How do you manage LLM API costs and implement caching to reduce redundant calls? What is LlamaIndex and how does it compare to LangChain for RAG use cases? What is the Hugging Face Hub and how do you push a trained model to share it? How do you build a demo web interface for an LLM application using Gradio? How do you monitor and debug LLM applications in production using LangSmith?
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