Question 1

What is a vector database and when do you actually need one?

Accepted Answer

**Target Audience:** Engineers starting a RAG or semantic search project who keep being told to "add a vector database" and want to know what that is and whether they need one yet.
**Difficulty:** Easy
**Verified with:** Node 22.22.0 and Python 3.12.3 (the sizing, filtered search and sync simulation below). The per-core speed in the sizing table is a stated assumption, not a measurement. Product facts are cited inline with the date checked (October 2026).

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

Question 2

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 3

Embeddings in 2025-26: how do you choose model, dimensions, and distance?

Accepted Answer

**Target Audience:** Engineers setting up retrieval who must commit to an embedding model, a vector size and a metric, and want a defensible way to choose rather than copying a blog post.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the truncation and quantization program below, on seeded synthetic vectors). Model facts, prices and limits are cited inline with the source and the date checked (October 2026); they change often, so treat them as examples of the mechanis

Question 4

What is the difference between fine-tuning, LoRA and prompt engineering?

Accepted Answer

**Target Audience:** Engineers who can call an LLM API and have heard "just fine-tune it", but have never trained a model or seen what LoRA actually changes.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (a toy linear layer trained with plain gradient descent; no real model is trained or called). Paper and library facts are cited inline with the date they were checked (October 2026).

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

Question 5

How do you manage conversations that outgrow the context window?

Accepted Answer

**Target Audience:** Engineers running a chat assistant or agent whose sessions get long, and who need a history strategy that is cheaper than "send everything" without making the bot forget.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the conversation replay below). Provider compaction features were checked against the Anthropic docs and OpenAI documentation summaries cited inline, in October 2026.

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

Question 6

Prompt caching in 2025: how do you cut cost with Anthropic & OpenAI caches?

Accepted Answer

**Target Audience:** Engineers who already know what prompt caching is and now have to make it pay on a real bill, on Anthropic, OpenAI or both.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the cost simulator below). Provider behaviour was checked in October 2026 against the Anthropic docs, OpenAI announcements, and the Azure and third-party documentation cited inline where OpenAI's own guide could not be read directly.

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

Question 7

What is context engineering and how does it differ from prompt engineering?

Accepted Answer

**Target Audience:** Engineers moving from single prompts to RAG systems and agents, who need to explain what changes when "the prompt" becomes a pipeline.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (a context assembler run over simulated candidate items with given token counts; no real model is called). Origins of the term and published guidance are cited inline, checked October 2026.

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

Question 8

How do you reduce hallucinations in a production LLM application?

Accepted Answer

**Target Audience:** Engineers who own an LLM feature that real users rely on (support bots, internal assistants, document Q&A) and need to make it stop inventing things, measurably.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the four-layer scoring run below). Case law, papers and provider features were checked via the sources cited inline, in October 2026.

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

Question 9

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 10

pgvector vs Pinecone vs Qdrant: how do you choose a vector DB in 2025?

Accepted Answer

**Target Audience:** Engineers and tech leads who have decided they need vector search and must pick between an extension in Postgres, a managed service and an open-source engine, then defend the choice.
**Difficulty:** Hard
**Verified with:** Node 22.22.0 and Python 3.12.3 (the filtered-search simulation below). Product behaviour, limits and pricing models are cited inline with the source and the date checked (October 2026). The title says 2025; the facts here are as found in October 2026, and

AI ENGINEERINGDECODED.

Easy

What is a vector database and when do you actually need one?

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

Medium

Embeddings in 2025-26: how do you choose model, dimensions, and distance?

What is the difference between fine-tuning, LoRA and prompt engineering?

How do you manage conversations that outgrow the context window?

Prompt caching in 2025: how do you cut cost with Anthropic & OpenAI caches?

What is context engineering and how does it differ from prompt engineering?

How do you reduce hallucinations in a production LLM application?

Hard

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

pgvector vs Pinecone vs Qdrant: how do you choose a vector DB in 2025?

What is quantization and when would you use a quantized model?

How do agents maintain memory across steps and sessions?