Skip to content

Tracks Drills Topics Technologies Tech stacks Skill areas

Tracks Drills Topics

Loading

Aporeon

Technical interview practice, from one developer to another.

About Editorial standards Support Privacy Terms Refund policy

Drills

Pick a focused question that fits your time, stack, and interview goal.

How much time do you have?

Show one-drill sessions you can finish now.

Search

Track

Practice style

Level

More filters1

Topic

Technology

Tech stack

Skill area

15 results across 1 active filter

Page 1 of 1

Comparison questionHard~12 min

Compare a hosted model API with a self-hosted or open-weight model.

Compares managed model APIs with operating open-weight models across control, expertise, privacy, scale, latency, cost, and portability.

Artificial IntelligenceGenerative AILarge Language Models

Comparison questionMedium

~10 min

Compare inference, training, fine-tuning, prompting, and RAG.

Separates runtime model use from model training, fine-tuning, prompt design, and retrieval-augmented generation as different engineering levers.

Artificial IntelligenceGenerative AILarge Language Models

Concept questionMedium~7 min

How do temperature and sampling settings affect model output?

Explains how decoding settings reshape token selection and why they must be tuned against task-specific evaluation rather than folklore.

Artificial IntelligenceLarge Language Models

Comparison questionHard~12 min

How do you balance quality, latency, and cost in an AI feature?

Balances AI quality, tail latency, and cost through explicit product thresholds, model choice, context control, caching, routing, and measurement.

Artificial IntelligenceGenerative AILarge Language Models

Debugging scenarioMedium~10 min

How should an application handle context overflow and truncated output?

Handles token-budget overflow and incomplete generation through explicit prioritization, rejection, retrieval, summarization, continuation, and validation.

Artificial IntelligenceLarge Language Models

Comparison questionMedium~10 min

How would you choose a model for a product feature?

Chooses a model from task-specific evaluation, operating constraints, safety, latency, cost, context, modality, and provider requirements.

Artificial IntelligenceGenerative AILarge Language Models

Concept questionEasy~5 min

What are tokens, and why do they matter in an AI application?

Explains tokens as model-specific text units and connects tokenization to context limits, output limits, latency, cost, and text behavior.

Artificial IntelligenceLarge Language Models

Concept questionEasy~5 min

What does hallucination mean in an AI application?

Defines hallucination as unsupported or incorrect model output and connects it to evidence, task design, evaluation, and user-visible uncertainty.

Artificial IntelligenceGenerative AILarge Language Models

Concept questionEasy~5 min

What goes into an LLM context window?

Describes the context window as the bounded request state containing instructions, conversation, retrieved evidence, tool results, and output capacity.

Artificial IntelligenceLarge Language Models

Concept questionEasy~5 min

What is a large language model, and what does it actually predict?

Defines a large language model through next-token prediction and connects that mechanism to useful language behavior and its limits.

Artificial IntelligenceGenerative AILarge Language Models

Comparison questionMedium~7 min

What is an embedding, and how is it different from generated text?

Defines embeddings as model-produced vectors used for similarity and contrasts representation with text generation.

Artificial IntelligenceLarge Language ModelsVector Databases

Comparison questionMedium~10 min

What is the difference between model capability and application reliability?

Separates what a model can perform from whether the complete AI feature behaves dependably under real inputs, failures, and constraints.

Artificial IntelligenceLarge Language Models

Comparison questionMedium~7 min

When should you not use an LLM?

Identifies tasks better served by deterministic code, search, rules, or conventional models because of correctness, latency, cost, or control.

Artificial IntelligenceLarge Language Models

Comparison questionMedium~7 min

When would you use a smaller model instead of the most capable one?

Uses task bounds and evaluation evidence to choose smaller models for lower latency, cost, capacity, privacy, or local execution.

Artificial IntelligenceGenerative AILarge Language Models

Concept questionMedium~7 min

Why can the same prompt produce different answers?

Connects probabilistic decoding and changing service conditions to output variation, reproducibility limits, and appropriate testing.

Artificial IntelligenceLarge Language Models