For NLP interviews in 2026, prepare for both the fundamentals—text representations, tokenization, and evaluation—and the engineering choices behind transformers, retrieval-augmented generation (RAG), and fine-tuning. This editorial selection prioritizes concepts that help candidates explain not just what a method is, but when it fits and where it can fail. The emphasis varies by role: an applied-LLM interview may spend more time on retrieval and production constraints, while an entry-level screen may focus on representations and metrics.
Quick guide: the 10 questions
| Question | Core concept | What a strong answer shows |
|---|---|---|
| 1. What is NLP? | Language analysis and generation | Can distinguish tasks and frame an application |
| 2. What are Bag-of-Words and TF-IDF? | Sparse text features | Understands useful baselines and their limits |
| 3. What are word embeddings? | Dense vector representations | Can compare one-hot, static, and contextual representations |
| 4. What is tokenization? | Converting text to model-readable units | Accounts for special tokens, length, and truncation |
| 5. How does self-attention work? | Context-dependent token representations | Explains the mechanism and its computational trade-off |
| 6. How do BERT, GPT, and encoder-decoder models differ? | Transformer architecture | Matches architecture to task |
| 7. How would you build a text classifier? | End-to-end supervised learning | Starts with the objective, data, and baseline |
| 8. What is named-entity recognition? | Entity-span labeling | Understands span-level evaluation and annotation issues |
| 9. How do you evaluate NLP and generative systems? | Task-specific measurement | Combines metrics, error analysis, and operational measures |
| 10. What is RAG, and when is it preferable to fine-tuning? | Retrieval plus generation | Separates knowledge access from model behavior |
1. What is natural language processing, and what is it used for?
Natural language processing (NLP) is the area of AI concerned with representing, analyzing, understanding, and generating human language. It draws on linguistics, statistics, machine learning, and deep learning.
Examples include text classification, sentiment analysis, named-entity recognition (NER), translation, summarization, question answering, information extraction, search, and text generation. It helps to distinguish related goals: natural-language understanding extracts information or intent; generation produces text; information retrieval finds relevant material; and language modeling estimates or generates token sequences. Current Hugging Face task documentation covers common transformer tasks such as classification, token classification, question answering, summarization, translation, and generation.
Example: A support-message classifier might assign a message to a queue, while a separate extraction step identifies the product and issue. A useful interview answer makes clear which outcome the system must produce rather than treating “NLP” as one task.
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2. What are Bag-of-Words and TF-IDF, and when would you use them?
Bag-of-Words (BoW) represents a document using vocabulary features—usually word counts or presence indicators—without preserving grammar or word order. TF-IDF reweights those features: a term receives more weight when it is frequent in a particular document but uncommon across the collection.
A common form is TF-IDF(t,d) = TF(t,d) × log(N / DF(t)), where t is a term, d a document, N the number of documents, and DF(t) the number containing the term. Exact formulas and smoothing conventions can vary.
- Useful for: fast, interpretable baselines for small or medium text-classification and search problems.
- Limitations: sparse, high-dimensional features; no inherent word-order or contextual meaning; “car” and “automobile” remain separate unless features bridge them.
- Practical trade-off: TF-IDF with logistic regression can be cheaper, faster, and easier to debug than a transformer, and may be adequate for a stable task.
N-grams can capture short phrases, at the cost of a larger feature space. Stop-word removal is task-dependent: words that are often uninformative can still matter for phrases, negation, or specialized text. If asked about BM25, note that it is a related term-weighting and ranking method rather than simply another name for TF-IDF.
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3. What are word embeddings, and how do they differ from one-hot encoding?
One-hot encoding assigns each vocabulary item its own sparse vector with one active position. It marks identity but not similarity: “cat” is no closer to “kitten” than to “airplane” in that representation. An embedding is a dense, lower-dimensional vector learned from data; words used in similar contexts may receive similar vectors.
Word2Vec, GloVe, and FastText are examples of static embedding approaches. A static method assigns a word one representation regardless of sentence context, so “bank” has the same vector in “river bank” and “bank account.” Contextual representations, such as those produced by BERT-style models, vary with surrounding text.
Embeddings are learned representations, not guarantees of universally correct meaning. They may reflect biases, domain associations, or artifacts in training data. Subword methods can help with rare or unseen words; FastText uses character-level information, while transformer tokenizers commonly split words into subword units.
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Likely follow-up: What is the difference between CBOW and Skip-gram in Word2Vec? Both learn word representations from context, but they differ in which context-related prediction is trained. A good answer should explain the training objective at a high level and connect it to how the learned vectors are used.
4. What is tokenization, and why does it matter?
Tokenization divides text into units a model can process. Depending on the model, these units can be words, subwords, characters, or bytes. Subword tokenization is common in transformers because it balances vocabulary size with the ability to represent rare or previously unseen words.
BERT uses WordPiece and task-related special tokens such as [CLS] and [SEP]. Tokenizer behavior is model-specific; use the tokenizer associated with the checkpoint rather than assuming different models share a vocabulary. The Transformers task documentation describes BERT’s use of WordPiece and special tokens.
For a concrete example, the official Hugging Face Pipeline tutorial demonstrates task-oriented inference, while this tokenizer example illustrates the inputs that models commonly need:
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
encoded = tokenizer(
"NLP interviews increasingly cover transformers.",
padding=True,
truncation=True,
return_tensors="pt"
)
print(encoded["input_ids"])
print(encoded["attention_mask"])Padding makes examples in a batch compatible in length; an attention mask distinguishes real tokens from padding. Truncation enforces a length limit, but can discard the part of a document that contains the answer. Token counts also affect context use and inference cost, and the same text can tokenize differently across models.
Likely follow-up: How would you handle long documents? Choose a strategy based on the task: preserve relevant sections, process chunks, or use a model and workflow designed for longer inputs. Check whether boundaries, overlap, and aggregation preserve the information the downstream task needs.
5. How does self-attention work?
Self-attention lets each token build a representation by weighting information from other tokens in the same sequence. In scaled dot-product attention, queries (Q) are compared with keys (K) to produce weights, which are applied to values (V):
Attention(Q, K, V) = softmax(QKᵀ / √dₖ)V
Here, dₖ is the key dimension; dividing by its square root helps keep dot-product magnitudes in a useful range for the softmax. For example, a model reading “The animal did not cross the road because it was tired” can use surrounding context when representing “it.”
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchMulti-head attention computes several attention patterns in parallel, allowing different learned relationships to contribute to a representation. It is tempting to label a particular head as a specific linguistic mechanism, but such interpretations should be treated cautiously.
- Strength: attention can relate distant tokens and supports parallel computation during training.
- Cost: standard attention can require substantial computation and memory as sequence length grows.
- Interpretation: attention weights alone are not a complete explanation of a model’s reasoning.
Likely follow-up: How does causal attention differ from bidirectional attention? Causal masking prevents a token from using future tokens, which supports left-to-right generation. Bidirectional encoders can use context from both sides of a token.
6. How do BERT, GPT-style models, and encoder-decoder models differ?
These names describe common architecture patterns and uses, not a claim that every model in a family behaves identically.
| Architecture | Typical context pattern | Common strength | Examples |
|---|---|---|---|
| Encoder-only | Bidirectional context | Understanding and representation tasks | BERT |
| Decoder-only | Causal, left-to-right | Autoregressive text generation | GPT-2 and GPT-style models |
| Encoder-decoder | Encodes an input, then generates an output | Sequence-to-sequence transformations | BART, T5 |
BERT-style pretraining commonly includes masked-language modeling: the model predicts hidden tokens using surrounding context. GPT-style causal language modeling predicts the next token from earlier ones. Encoder-decoder models learn to transform an input sequence into an output sequence. According to Hugging Face’s task guide, BERT is used for classification, token classification, and question answering; GPT-2 for generation; and BART for tasks such as summarization and translation.
The original BERT paper describes fine-tuning a pretrained model for several NLP tasks with an added output layer and limited task-specific architectural changes. In practice, select an architecture for the input-output pattern: understanding or classifying a sequence differs from generating a continuation or transforming one sequence into another.
Likely follow-up: Why is BERT not usually used for free-form generation? Its bidirectional encoder objective is not the same as next-token autoregressive decoding, which is the usual setup for generating a continuation. Explain the task and objective rather than saying one family is universally better.
7. How would you build a sentiment-analysis or text-classification system?
Start with the decision the system must support. Then work through data, a baseline, model choice, evaluation, and deployment. For sentiment, define what positive, negative, and neutral mean in the application: sentiment can be target-specific, and a message can praise one product while criticizing another.
- Define the objective and labels. Agree on what counts as a correct prediction and how ambiguous examples should be annotated.
- Inspect the data. Check label quality, class balance, duplicates, privacy constraints, and whether examples represent production traffic.
- Split carefully. Create training, validation, and held-out test sets. Use time-based or group-based splits when random splitting could leak near-duplicates or future information.
- Set a baseline. Try an approach such as TF-IDF plus logistic regression before adding more complexity.
- Choose and train a model. Consider a pretrained transformer when context matters and the task warrants the serving cost and complexity.
- Evaluate errors and difficult slices. Examine a confusion matrix, minority classes, sarcasm, domain language, and examples where the label depends on a target.
- Plan deployment. Measure latency and cost, monitor data and performance shifts, and define how the model will be reviewed or updated.
For a quick inference demonstration, Hugging Face’s Pipeline API tutorial shows task-specific pipelines and model selection. A short example is:
from transformers import pipeline
classifier = pipeline("sentiment-analysis")
print(classifier("The support team solved my issue quickly."))This is an inference illustration, not evidence that a default checkpoint has been validated for a particular organization, label scheme, or language. Assess a candidate model on representative labeled data before relying on it.
Likely follow-up: What if classes are imbalanced? Accuracy may conceal poor performance on a rare class; inspect per-class precision and recall, choose a threshold against the application’s error costs, and use suitable sampling or weighting during training if justified.
8. What is named-entity recognition, and how is it evaluated?
NER finds spans of text and labels them with types such as person, organization, location, date, product, or a domain-specific category. In “Microsoft opened an office in Seattle,” a system might label “Microsoft” as an organization and “Seattle” as a location. NER is commonly framed as token classification or span extraction; Hugging Face’s task documentation includes NER among token-classification examples.
Entity-level precision, recall, and F1 are generally more informative than token accuracy alone. A strict evaluation may count an entity as correct only when both its boundaries and type match the reference.
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- Partial-span matches can be useful diagnostically but may not count as exact entity matches.
- Nested or discontinuous entities need explicit annotation conventions and may not fit simple token-tagging schemes cleanly.
- Abbreviations, new product names, rare entity classes, and inconsistent guidelines can all reduce reliability.
Likely follow-up: What do BIO tags mean? They encode whether a token is outside an entity, at its beginning, or inside it; related schemes add distinctions such as an entity’s final token. The answer should also acknowledge that tag conventions do not remove the need for clear span guidelines.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. How do you evaluate NLP and generative-AI systems?
Choose metrics to match the task, then pair them with error analysis and checks on real-world behavior. Useful options include:
| Task | Metrics or evaluation |
|---|---|
| Classification | Accuracy, precision, recall, F1, ROC-AUC, PR-AUC |
| NER | Entity-level precision, recall, F1 |
| Machine translation | BLEU plus task-specific human evaluation |
| Summarization | ROUGE, factuality checks, human evaluation |
| Language modeling | Perplexity, interpreted as predictive likelihood rather than overall usefulness |
| Retrieval | Recall@k, precision@k, MRR, nDCG |
| Question answering | Exact match, token-level F1, and groundedness where relevant |
| Generation | Helpfulness, relevance, factuality, toxicity, latency, and cost |
No single score establishes that a production system is good. Use a held-out test set, representative examples, slice-based performance checks, error analysis, and human review when judgments are subjective. Include robustness and safety tests, and measure latency and cost. After deployment, monitor behavior rather than treating offline results as permanent.
- BLEU and ROUGE measure forms of overlap with reference text; neither guarantees factual correctness.
- Perplexity measures how well a model predicts tokens, not whether it follows instructions or gives useful answers.
- LLM-as-judge scoring can help with evaluation, but may favor certain styles or reflect judge-model artifacts.
- For RAG, measure whether relevant evidence is retrieved separately from whether the generated answer uses it correctly.
Likely follow-up: How do you evaluate hallucinations? Define what counts as an unsupported claim, use evidence-linked examples and human review where needed, and assess both answer quality and whether the supplied evidence supports the answer. A generic generation score is not enough.
10. What is RAG, and when should you use it instead of fine-tuning?
Retrieval-augmented generation combines a language model with a search or retrieval system. At query time, the system finds relevant documents or passages and supplies them as context for generating an answer. The original RAG paper describes combining a pretrained model’s parametric memory with non-parametric memory in a dense vector index.
RAG and fine-tuning solve different problems. RAG supplies information at answer time; fine-tuning updates model parameters using examples. Choose based on what must change:
| Need | Approach to consider first |
|---|---|
| Frequently changing facts | RAG |
| Answers grounded in company documents or citations | RAG |
| Private knowledge that changes often | RAG |
| Specific response style or output behavior | Prompting or fine-tuning |
| A specialized task format | Fine-tuning may fit |
| Both domain knowledge and a specific response style | A combination may be appropriate |
RAG is not a guarantee against hallucination. It can fail when chunking loses context, retrieval misses the right passage, sources conflict, the context is overloaded, or the model ignores evidence. Vector search alone may also miss exact product codes or legal wording; keyword search alone may miss semantically related phrasing.
A credible system-design answer should discuss hybrid keyword and vector retrieval, metadata filters, reranking, document freshness, access control, provenance or citations, and evaluation of retrieval separately from answer generation. Include tests for prompt injection in retrieved documents and for what happens when no supporting evidence is found. Fine-tuning is conditional too: it is most promising when high-quality representative examples teach a repeated behavior or task that simpler prompting and retrieval do not address.
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Likely follow-up: How do you choose chunk size? Start from the document structure and the questions users ask, then compare chunking strategies on retrieval and answer quality. Consider overlap, metadata, context limits, and whether splitting separates a fact from the qualifications needed to interpret it.
Prepare for the interview you are taking
Entry-level and internship interviews
Be ready to explain preprocessing choices, BoW and TF-IDF, embeddings, classification, train-validation-test splits, and basic metrics. Practice building a simple baseline and explaining how you would inspect its errors.
NLP or machine-learning engineer interviews
Add transformer architecture, fine-tuning, data pipelines, error analysis, serving constraints, and monitoring. Be prepared to justify model complexity against latency, memory, cost, and maintainability.
Applied-AI and LLM interviews
Practice RAG system design, hybrid retrieval, reranking, grounding, prompt-injection risks, evaluation, context limits, cost, and failure handling. Explain how you would know whether retrieval or generation caused an incorrect answer.
Rapid revision: key NLP terms
| Concept | Reminder |
|---|---|
| TF-IDF | Weights terms that are distinctive to a document within a collection. |
| Embedding | A dense numerical representation learned from data. |
| Tokenization | Converts text into model-readable units. |
| Attention | Weights relationships among tokens to build representations. |
| BERT | An encoder-only model commonly used for understanding tasks. |
| GPT-style model | A decoder-only autoregressive generator. |
| NER | Identifies and labels entity spans. |
| F1 | The harmonic mean of precision and recall. |
| RAG | Retrieves external context before generation. |
| Fine-tuning | Updates model parameters using task-specific examples. |
How to answer an unfamiliar NLP question
- Clarify the user or business objective and define success.
- State assumptions about data, labels, language, and constraints.
- Propose a simple baseline before a complex architecture.
- Explain what data and model approach fit the task.
- Name metrics and describe how you would inspect failures.
- Address deployment constraints such as latency, privacy, cost, and monitoring.
This structure keeps an answer grounded in engineering decisions rather than a definition alone.
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