Question Answering
Getting a machine to answer a question in natural language — the task that quietly turned from "find the passage" into "generate the answer," and defines how we use AI today.
Reviewed July 16, 2026Stable
Reach for something else instead
- Extractive QA when you need the answer grounded in a specific passage.
- Semantic search when the user wants relevant documents, not a synthesised answer.
- RAG as the standard bridge — retrieve, then generate from what was retrieved.
Sources & further reading
- Rajpurkar et al. (2016), SQuAD: 100,000+ Questions for Machine Comprehension — the benchmark that defined extractive QA.
- Chen et al. (2017), Reading Wikipedia to Answer Open-Domain Questions — the retriever-reader architecture behind RAG.
- Lewis et al. (2020), Retrieval-Augmented Generation — grounding generative QA in retrieved documents.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Using generative QA where answers must be verifiable, and getting confident hallucinations.
- Blaming the model for wrong answers when the retrieval step failed to surface the right passage.
- Evaluating open-ended answers by exact string match, which misses correct paraphrases.
At a glance
FieldLanguage & LLMs
Two modesextractive (find the span) vs. generative (compose the answer)
Grounding fixRAG
DifficultyIntermediate