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Python / Python Modern Generative AI and Agents Interview Questions

How does tokenisation work in Hugging Face and what are the key tokenizer concepts?

Tokenisation converts raw text into integer IDs that the model can process. Modern LLMs use subword tokenisation (BPE, WordPiece, or SentencePiece) rather than word or character tokenisation, balancing vocabulary size against the number of tokens per sentence. Each model family has its own tokeniser trained alongside its vocabulary — you must always use the matching tokeniser for a given model.

Key concepts to understand: special tokens ([CLS], [SEP], <s>, </s>, <pad>) mark sentence boundaries and padding; attention masks are binary tensors that tell the model which positions are real tokens (1) vs padding (0); padding and truncation unify variable-length inputs into fixed-size batches; fast tokenizers (Rust-backed) are 10–100× faster than their Python equivalents.

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')

# Encode a single sentence
text = 'Hugging Face makes NLP easy.'
encoding = tokenizer(text, return_tensors='pt')
print(encoding.keys())
# dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])
print(encoding['input_ids'])
# tensor([[ 101, 17662, 2227, 3084, 17953, 2109, 1012,  102]])

# Decode back to text
print(tokenizer.decode(encoding['input_ids'][0]))
# [CLS] hugging face makes nlp easy. [SEP]

# Batch encoding with padding and truncation
texts = [
    'Short text.',
    'This is a much longer piece of text that goes on and on.',
]
batch = tokenizer(
    texts,
    padding=True,          # pad shorter sequences to the length of the longest
    truncation=True,       # truncate sequences longer than max_length
    max_length=128,
    return_tensors='pt',   # return PyTorch tensors
)
print(batch['input_ids'].shape)      # (2, 128)
print(batch['attention_mask'])        # 1 for real tokens, 0 for padding

# Token-level operations
tokens = tokenizer.tokenize('unbelievably')
print(tokens)   # ['un', '##believe', '##ably']  — WordPiece subwords

# Count tokens before calling API (avoid surprises)
n_tokens = len(tokenizer.encode('Hello world'))
print(f'{n_tokens} tokens')

Why must you use the exact tokenizer that matches a specific model checkpoint?
What does the attention_mask tensor tell the transformer model?

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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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