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Question-answering action

As explained in Effective Conversational AI

A question-answering action is a conversational action that detects an intent and provides an answer.

When users ask routine questions, a question-answering action detects the intent and returns a prepared answer, avoiding a longer dialogue. section 2.1.2

Configure each action with three practical elements:

  • Related user utterances that trigger intent recognition. section 2.1.2
  • An intent representing the user’s question. section 2.1.2
  • A response, usually simple static text; no conditional logic or next step is required. section 2.1.2

Cake Shop FAQ

Utterances such as “What are your store hours?” map to a store-hours intent and produce “We are open Monday through Friday, 9:00 a.m. to 9:00 p.m.” Testing an unrelated question prompts the bot to request a rephrasing. section 2.1.2

Table 2.2 Initial set of FAQ intents, with associated utterances and responses
Intent
Example utterances
Response
#background
Tell me about Cake Shop
What's the background on your business?
History of Cake Shop
Founded by Grandma Cake in 1980, we've made over 10,000 cakes for local residents!
#store_hours
Store hours
What are your store hours?
When are you open?
We are open Monday through Friday, 9:00 a.m. to 9:00 p.m.
#cake_options
Cake options
Do you make wedding cakes?
What kinds of cakes do you sell?
We offer cakes for many occasions, such as weddings, birthdays, anniversaries, retirement, and all- occasion cakes.
#cost
How much does a cake cost?
Is there a minimum order value?
Is there a surcharge for delivery?
Our cakes typically cost around $30, with a $5 delivery fee.
#cake_club
Cake rewards
Cake Club
Any special promotions or discounts?
Our Cake Club rewards program earns you a $10 gift certificate for every ten cakes you purchase.

Browse the FAQ table to compare intents, example utterances, and prepared responses. section 2.1.2

How Effective Conversational AI covers it

How deep it goes
Defines triggers, intent detection, response, and testing, while stopping short of conditional logic or multi-step dialogue.
Examples
Uses Cake Shop questions about background, store hours, cake options, cost, and rewards.
What it assumes
Assumes a conversational AI platform while introducing intents, utterances, responses, and actions as basic building blocks.
What you can do after
Builds and tests simple FAQ actions using example utterances and static responses.
How it connects
Connects user utterances, intents, responses, chatbots, and generative AI.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Effective Conversational AI. The section links point to the parts of the book it draws on.