Question 1

How do multimodal models handle images, audio and other non-text inputs?

Accepted Answer

**Target Audience:** Engineers who have used text-only LLM APIs and now need to send screenshots, scans, photos or audio.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the patch-projection demo and the token estimators below; no real model is called). Provider token rules and paper details are cited inline with the source and the date checked (October 2026).

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

Question 2

What is prompt caching and how does it reduce cost and latency?

Accepted Answer

**Target Audience:** Engineers who call an LLM API with long, repeated prompts (agents, chat assistants, RAG over a fixed corpus) and want to understand what the "cached tokens" line on their bill means.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (the prefix-cache simulation below). Provider behaviour and prices were checked against the Anthropic docs, OpenAI announcements and the other sources cited inline, in October 2026.

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

Question 3

How does the transformer architecture and attention work?

Accepted Answer

**Target Audience:** Engineers who know what an LLM does from the outside and want to understand the inside well enough to explain it on a whiteboard.
**Difficulty:** Medium
**Verified with:** Node 22.22.0 and Python 3.12.3 (attention computed by hand on 4 tokens; no real model). Paper facts are cited inline, checked October 2026.

> **How to read this doc:** Every idea is explained in plain language first, then tagged with `📌 Interview term:` so you also learn the exact words an interviewer ex

AI ENGINEERINGDECODED.

Medium

How do multimodal models handle images, audio and other non-text inputs?

What is prompt caching and how does it reduce cost and latency?

How does the transformer architecture and attention work?