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Glossary

Question answering

What is Question answering?

Question answering (QA) is a field of natural language processing in which a system answers questions posed in everyday language directly, rather than returning a list of search results. Modern systems look for relevant passages in a knowledge base and compose an answer from them, ideally with a source reference. Typical uses are customer service, internal knowledge bases and manuals – for instance asking the staff handbook ‘How many days’ leave do I get in my first year?’

How KI.ordo helps

KI.ordo agents answer questions from the knowledge you provide, cite their sources and say openly when they cannot find anything there.

Learn more →: Question answering

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Topics

  • NLP
  • Knowledge management
  • Search

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Related terms

Retrieval-augmented generation (RAG)

Retrieval-augmented generation combines search with text generation: before answering, the system searches a knowledge collection – such as manuals, policies or FAQs – for relevant passages and passes them to the language model as context. The answer is therefore based on current, company-specific content rather than only on the model's training knowledge. This reduces invented statements, makes sources traceable and allows updates without retraining.

  • RAG
  • knowledge search
  • +1

Information Retrieval

Information retrieval covers methods that find the documents or passages in a large body of text that match a query and rank them by relevance. Classic approaches such as BM25 weight search terms by how often and how rarely they occur; newer ones add semantic similarity. It underpins search engines, enterprise search and retrieval-augmented generation, in which a language model uses the retrieved passages as the basis for its answer.

  • Search
  • BM25
  • +1

Natural Language Understanding (NLU)

Natural language understanding is the part of language processing that captures the meaning of an utterance: what intent lies behind it, which people, places or dates are mentioned, and what is its tone? Together with natural language generation (NLG), NLU forms one of the two sides of NLP. One example is a customer service system that recognises “My parcel still hasn't arrived” as a delivery enquiry.

  • NLP
  • Intent detection
  • +1

Multi-hop reasoning

Multi-hop reasoning is an AI system’s ability to answer a question through several successive inferences, often combining information from different sources. Example: “Which department is headed by the person responsible for project X?” first requires identifying the responsible person and then their department. Such questions are demanding for search and RAG systems because every intermediate step must be found and linked correctly.

  • reasoning
  • question answering
  • +1