Question 1

What is RAG and why use it instead of fine-tuning for knowledge?

Accepted Answer

**Target Audience:** Beginners meeting retrieval-augmented generation for the first time, and engineers who need to defend "RAG, not fine-tuning" for a knowledge feature in an interview.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (a keyword retriever plus deterministic stub models; no real model or vector database is called). Papers cited inline were checked in October 2026.

> **How to read this doc:** Every idea is explained in plain language first, then tagged with

Question 2

Hybrid search: why combine BM25 with vectors and how does RRF work?

Accepted Answer

**Target Audience:** Engineers implementing or tuning hybrid retrieval who need to explain, and compute by hand, how two ranked lists become one.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (raw-score sum, min-max convex combination and RRF with different k, weights and windows, on two fixed result lists; no search engine is called). Engine parameters and papers were checked in October 2026.

> **How to read this doc:** Every idea is explained in plain language first

Question 3

Reranking in RAG: when and how to add a cross-encoder?

Accepted Answer

**Target Audience:** Engineers who already understand what a reranker is and now have to decide whether to put one into a real RAG system, which one, how deep, and inside what latency budget.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the depth sweep and timeout simulation below). The relevance scores are simulated with a seeded generator and the reranker latency comes from a stated cost model, so the numbers show the *method*, not a benchmark of any product. Vendo

Question 4

What is reranking and why add it after retrieval?

Accepted Answer

**Target Audience:** Engineers who have a working vector search or RAG prototype and keep hearing that "adding a reranker" is the cheapest quality win, and want to know what that actually means.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the two-stage retrieval demo below). The reranker in the demo is a hand-written pair scorer standing in for a model, so it shows the *shape* of reranking, not the quality of a real cross-encoder. Product facts are cited inline with

Question 5

How do you scope retrieval with metadata filtering and multi-tenancy?

Accepted Answer

**Target Audience:** Engineers putting RAG into a multi-customer (B2B) product, or into a company where not every employee may read every document.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the filtered-search and authorization simulation below). Vector database behaviour is cited from vendor documentation with the date checked (October 2026); the SQL snippets are labelled "not run here".

> **How to read this doc:** Every idea is explained in plain language first

Question 6

Evaluating RAG with Ragas: faithfulness, answer relevancy, context precision?

Accepted Answer

**Target Audience:** Engineers who run or are about to run Ragas on a RAG system and want to know what each number means, how it is computed, and what to fix when it moves.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (a hand-computed version of the four formulas below). Metric definitions were checked against the Ragas documentation pages for v0.2 to v0.4 via web search in October 2026; API details that only appeared in third-party guides are marked as such.

> **How

Question 7

What is hybrid search and why combine BM25 with vector search?

Accepted Answer

**Target Audience:** Engineers whose embedding-only RAG search misses exact terms, and anyone who needs to explain lexical vs semantic retrieval and why teams combine them.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (BM25 written from scratch, a deterministic stub embedding, and RRF fusion on an eight-article knowledge base; no model or search engine is called). Engine facts were checked in each vendor's documentation in October 2026.

> **How to read this doc:** Ev

Question 8

How would you design a production RAG pipeline end-to-end in 2025-26?

Accepted Answer

**Target Audience:** Engineers preparing for an AI or backend system design round who can explain RAG in one sentence and now need to design the whole thing: ingestion, retrieval, generation, numbers, operations and evaluation.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the miniature pipeline and the sizing arithmetic below). Every input to the sizing is a stated assumption, and the prices in it are illustrative placeholders, not a vendor price list. Product facts ar

Question 9

What are common RAG failure modes and how do you debug them?

Accepted Answer

**Target Audience:** Engineers who run or are about to run a RAG feature in production and need a systematic way to turn "the bot got it wrong" into a specific, fixable cause.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the chunk-boundary demo and the triage harness below). The request logs in the triage are hand-written examples of each failure type, not data from a real system. Research findings are cited inline.

> **How to read this doc:** Every idea is explained

AI ENGINEERINGDECODED.

Easy