Skip to content

Tracks Drills Topics Technologies Tech stacks Skill areas

Tracks Drills Guides

Loading

Aporeon

Technical interview practice, from one developer to another.

Guides 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

8 results across 1 active filter

Page 1 of 1

Coding problemHard~40 min

Build hybrid RAG retrieval with access control and verified citations

Builds a hybrid retrieval pipeline that enforces tenant scope before ranking, assembles a bounded context, and verifies returned citations.

Artificial IntelligenceGenerative AILarge Language ModelsVector Databases

Comparison question

Medium

~10 min

Compare keyword, vector, and hybrid search for RAG.

Combines exact lexical matching with semantic recall and explains why hybrid retrieval often needs measured fusion and ranking.

Artificial IntelligenceGenerative AILarge Language ModelsVector Databases

Architecture scenarioMedium~10 min

How would you choose a chunking strategy for a RAG system?

Balances retrieval precision, answer completeness, document structure, overlap, metadata, and model input limits.

Artificial IntelligenceGenerative AILarge Language ModelsVector Databases

Architecture scenarioHard~15 min

How would you migrate an embedding model or vector index safely?

Rebuilds and compares incompatible vector spaces using versioned indexes, shadow queries, atomic cutover, and rollback.

Artificial IntelligenceGenerative AILarge Language ModelsVector Databases

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

Concept questionMedium~10 min

When should metadata filters be applied in vector retrieval?

Uses pre-filtering for authorization and hard constraints while recognizing engine-specific recall and performance behavior.

Artificial IntelligenceGenerative AILarge Language ModelsVector Databases

Concept questionMedium~10 min

When would you rerank retrieved results before generation?

Uses a more precise second-stage ranker on a bounded candidate set while accounting for latency, cost, and authorization.

Artificial IntelligenceGenerative AILarge Language ModelsVector Databases

Concept questionMedium~7 min

Why preserve document structure and parent-child relationships in RAG?

Uses hierarchy and adjacency to recover context that flat chunks lose while avoiding indiscriminate document expansion.

Artificial IntelligenceGenerative AILarge Language ModelsVector Databases