Level C1 (AI & RAG Q&A)290 words • ~2 min
RAG & AI Search Systems: Technical Interview Q&A
By AI Engineering Series
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Technical Interview Questions & Model Answers
Q1What is Retrieval-Augmented Generation and how does it prevent LLM hallucinations?
Answer: RAG retrieves verifiable facts from an external knowledge base and injects them into the prompt, grounding language model responses in accurate context rather than parametric memory.
Q2What strategies determine optimal document chunking for vector search?
Answer: Optimal chunking balances semantic coherence and token length, using paragraph boundaries and overlapping windows so individual chunks preserve context without diluting embedding specificity.
Q3How does dense vector retrieval capture semantic meaning beyond keyword search?
Answer: Dense retrieval maps text into continuous high-dimensional vector spaces, calculating cosine similarity to match conceptually related sentences even when no shared keywords exist.
Q4Why is cross-encoder reranking used as a second stage in retrieval pipelines?
Answer: While bi-encoder vector search rapidly gathers hundreds of candidates, a cross-encoder performs deep joint attention across query-passage pairs to surface the most relevant context.
Q5How does hybrid search combine Elasticsearch lexical search with dense vector indexing?
Answer: Hybrid search fuses BM25 term frequency scores with vector similarity metrics, guaranteeing exact matches for specific identifiers while maintaining broad semantic understanding.
Q6Why should cloud AI services use Managed Identity rather than static API keys?
Answer: Managed identities provide automatically rotated Azure Active Directory tokens, eliminating hardcoded secrets in source code and preventing credential leakage across CI environments.
Key Vocabulary in this Story
retrieval/rɪˈtriː.vəl/noun
The process of searching for and obtaining specific data from a knowledge base.
truy xuất dữ liệu
embedding/ɪmˈbed.ɪŋ/noun
A dense, high-dimensional numerical vector representation of semantic meaning.
nhúng vector (vector embedding)
reranking/riːˈræŋ.kɪŋ/noun
A secondary scoring step that reorders retrieved search documents using a more expressive scoring model.
tái xếp hạng kết quả
hallucination/həˌluː.səˈneɪ.ʃən/noun
Plausible-sounding but factually incorrect outputs generated by AI language models.
ảo giác AI (thông tin sai lệch)