Strong answers to GenAI and LLM interview questions connect model concepts to engineering decisions: what a design does, where it can fail, and how you would measure or operate it. These seven questions cover Transformers, tokenization, retrieval-augmented generation (RAG), embeddings, evaluation, RLHF, and deployment.
1. How does a Transformer produce context-aware token representations, and how do encoder-only and decoder-only designs differ?
Explain how context enters a representation
A useful answer follows the data through the model. Text is split into tokens, which are mapped to learned embeddings. Positional information helps the model account for token order. In self-attention, each token’s representation is updated using information from other tokens, weighted by how relevant they are to interpreting it. Stacked Transformer layers repeat and refine this process.
Attention also creates an engineering constraint: its work and memory demands grow roughly quadratically with sequence length in the standard formulation. Longer inputs can therefore increase latency and memory pressure, even before generation begins.
Contrast the common architecture families
| Architecture | Typical role | What to say in an interview |
|---|---|---|
| Encoder-only | Building representations of input text | It reads the input to produce context-aware representations; it is commonly suited to understanding tasks. |
| Decoder-only | Generating continuations | It predicts the next token from prior context and is the common pattern behind generative LLMs. |
| Encoder-decoder | Mapping an input sequence to an output sequence | An encoder represents the input and a decoder generates an output conditioned on it. |
A strong response distinguishes what each design is built to do without claiming that architecture alone determines product quality. Connect the choice to the task and to sequence-length costs.
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2. Why do LLMs tokenize text into subwords, and what engineering trade-offs does the tokenizer create?
Explain the vocabulary trade-off
Tokenizers such as BPE, Unigram, and WordPiece split text into units that are often smaller than whole words. A subword vocabulary can stay manageable while still representing rare or previously unseen words as combinations of familiar pieces. The trade-off is that a word or phrase does not necessarily correspond to one token, and different tokenizers can split the same text differently.
Connect tokenization to system behavior
- Context capacity: A context window holds tokens, not words or characters. Text that produces more tokens consumes more of the available input budget.
- Cost and latency: Where usage or processing is token-based, token count affects the amount of work and may affect cost and response time.
- Language coverage: Tokenizer behavior can differ across languages and writing systems, changing how efficiently text is represented.
- RAG chunking: Chunk boundaries should be checked against the tokenizer used by the model. A chunk that looks short in characters may still use many tokens, and splitting at awkward points can harm retrieval context.
In an interview, say how you would measure token counts on representative inputs rather than assuming a fixed word-to-token ratio. Mention that tokenizer and model compatibility matters when loading a model or building its inference pipeline.
3. Design a RAG system for a changing knowledge base. Where can it fail, and how would you diagnose the failures?
Describe the flow and freshness mechanism
RAG retrieves relevant external content, adds it to the model’s prompt, and generates an answer. For a changing knowledge base, document ingestion and index refresh are part of the design: external context can reflect updates without retraining the model, but only after the changed material has been processed and made searchable.
Rank #2
- Prepare documents: Parse the source material, preserve useful structure and metadata, and split it into chunks sized for the retrieval and prompt constraints.
- Index the content: Create embeddings and store them with identifiers and metadata needed for filtering, updates, and citations.
- Retrieve candidates: Search using the user’s query; apply metadata filters where appropriate, then consider reranking candidates if initial similarity results are not sufficiently relevant.
- Assemble the prompt: Include the most useful passages and instructions to answer from that material. Keep source identifiers so the response can be grounded and cited.
- Generate and verify: Check whether the answer is supported by the retrieved passages and whether citations point to the claims they accompany.
Separate retrieval problems from generation problems
- Retrieval failure: The needed passage is missing, stale, noisy, or ranked too low. Inspect the indexed documents, chunk boundaries, metadata filters, query, and retrieved candidates. If the evidence is absent, changing the prompt alone will not recover it.
- Generation failure: The retrieved evidence is correct, but the model ignores it, misreads it, or makes unsupported claims. Inspect prompt assembly and answer behavior; test whether clearer source instructions, reduced context noise, or a different generation setup improves grounding.
Build a small, versioned set of representative questions with expected source documents and acceptable answers. Run it after ingestion, indexing, retrieval, or prompt changes. This makes it easier to tell whether a regression came from finding the evidence or using it.
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Work from the query to the indexed content
Start by examining how documents are parsed and chunked, because an embedding model cannot recover content lost during extraction or split away from the context needed to interpret it. Then choose an embedding model for the relevant language coverage and task, create a vector index, and select a nearest-neighbor search approach. Depending on the use case, add metadata filters or a reranker before assembling results for the application.
Make the trade-offs measurable
Compare candidate pipelines on a representative query set, including difficult queries and hard negatives—items that appear similar but are not actually relevant. Measure whether the desired documents appear among the results, how much irrelevant material is returned, and the resulting answer quality if retrieval feeds an LLM. Track latency, throughput, index memory, and operating cost alongside retrieval quality; a small quality gain may not justify a large operational cost for every application.
Rank #3
Also check language coverage and how the system behaves as documents and user queries change. Monitor query distributions and index drift, and retain test cases that expose known failure patterns. Do not choose an embedding model on a single favorable example or a generic similarity score detached from the application’s relevance criteria.
5. How would you evaluate an LLM or RAG application before and after a change?
Keep retrieval and generation tests distinct
For RAG, one test set should assess retrieval—whether useful evidence is found—and another should assess generated answers—whether they correctly use that evidence. This separation helps locate regressions instead of treating the final answer as one opaque score. For a standalone LLM, build tests around the actual task and expected behavior.
Use a balanced evaluation set
- Retrieval: Measure hit or recall behavior against expected documents, and examine irrelevant results that add noise.
- Answers: Assess correctness, faithfulness to the supplied sources, and whether citations accurately support the claims.
- Safety and fairness: Include safety cases, refusal behavior, and tests for unfair or inconsistent outcomes relevant to the application.
- Operations: Track latency and cost so a quality improvement is considered alongside its runtime impact.
- Robustness: Include adversarial inputs, edge cases, and regression examples drawn from prior failures.
Run the same suite before and after a model, prompt, retrieval, or infrastructure change. Automated evaluators can make comparisons repeatable, but important failures should still be reviewed against clear criteria. For candidate models, side-by-side comparisons on the same cases can expose differences that a single aggregate score hides.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. What is RLHF, what signal does it provide, and what can go wrong when using it for alignment?
Explain the learning signal
Reinforcement learning from human feedback (RLHF) uses human judgments about model responses to shape model behavior. A common setup collects a prompt, multiple candidate responses, and human preferences between them. Those preferences can be used to train a reward or preference model, which then guides policy optimization. Related preference-training approaches may use the preference signal differently, so do not imply every alignment pipeline uses an identical sequence of steps.
Preference data can cover dimensions such as helpfulness, accuracy, safety, writing quality, and task completion. It supplies a signal about which responses people prefer; it does not establish that a response is objectively true or that the resulting model is safe in every situation.
Name the risks and the checks
- Annotator disagreement: People may reasonably prefer different answers, so labels can be inconsistent.
- Bias in the feedback: Preferences can reflect annotator, cultural, or task-specific assumptions that do not generalize to all users.
- Reward hacking and over-optimization: A model may learn to score well against the preference signal without improving the qualities the signal was meant to capture.
A strong answer recommends documenting the preference dimensions, examining disagreement, and retaining held-out tests for factuality and safety. Those evaluations test properties that preference optimization alone cannot guarantee.
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7. How would you take an open LLM from a model repository to a dependable inference service?
Load and serve the model deliberately
Begin by confirming that the model and tokenizer are compatible, then load them with the appropriate configuration. In a Transformers-based workflow, the AutoTokenizer and AutoModel families support loading from a repository; inputs must be tokenized into tensors, and generation is configured through the model’s generation controls. Device placement can be specified or allocated automatically where supported, but the actual hardware and memory available still constrain what can be served.
Design batching and streaming around the application’s latency and throughput requirements. Set timeouts, and cap input context and output lengths so a single oversized request cannot consume unbounded resources. Cache repeated prompts when it is safe and useful to do so, taking care that user-specific or sensitive content is not exposed across requests.
Make the service observable and recoverable
- Record latency and token usage, and monitor failures and resource pressure.
- Enforce access controls and the application’s content and privacy policies.
- Maintain a regression suite for representative quality, safety, and edge cases.
- Use staged changes and a rollback path so a model, configuration, or serving change can be reversed if it degrades behavior.
A dependable service is more than a successful model load: it includes bounded requests, predictable generation settings, operational monitoring, and a way to detect and recover from regressions.
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