Phase 2: Sequence Models · ~75 minutes · Python

Question Answering Systems

Three systems shaped modern QA. Extractive found spans. Retrieval-augmented grounded them in documents. Generative produced answers. Every modern AI assistant is a mix of the three.

Hiring signal: Understanding of question answering systems internals

What you will learn

Implement question answering systems from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build Languages: Python Prerequisites: Phase 5 · 11 (Machine Translation), Phase 5 · 10 (Attention Mechanism) Time: ~75 minutes

The Problem

A user types "When did the first iPhone launch?" and expects "June 29, 2007." Not "Apple's history is long and varied." Not "2007" sitting in isolation with no sentence. A direct, grounded, correct answer.

Three architectures have dominated QA over the last decade.

Extractive QA. Given a question and a passage that is known to contain the answer, find the start and end indices of the answer span in the passage. SQuAD is the canonical benchmark.
Open-domain QA. The passage is not given. Retrieve the relevant passage first, then extract or generate an answer. This is the bedrock of every RAG pipeline today.
Generative / Closed-book QA. A large language model answers from its parametric memory. No retrieval. Fastest at inference, least reliable on facts.

The trend in 2026 is hybrid: retrieve the best few passages, then prompt a generative model to answer grounded in those passages. That is RAG, and lesson 14 covers the retrieval half in depth. This lesson builds the QA half.

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You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

RAG (Retrieval-Augmented Generation)