# Source: https://docs.avaamo.com/user-guide/overview-and-concepts/quick-summary.md

# Concepts summary

Before getting into a deep-dive understanding of the key concepts used in the Avaamo Conversational AI Platform, here is a quick summary of key concepts and terms:

<table><thead><tr><th width="150" align="center">Concept</th><th width="603.4285714285713">Description</th></tr></thead><tbody><tr><td align="center"><a href="agents">Agents</a></td><td><p>An agent is synonymous with a "human agent". Similar to a human agent that has or is trained with different skills for responding to user queries, an agent is a "container of skills", each specialized to understand and handle a specific task in the user conversation flow.  </p><p></p><p><strong>Example</strong>: A Pizza agent can contain Membership and Rewards policy Answers skill, Order Pizza Dialog skill, FAQ Q&#x26;A skill, and Smalltalk skill for responding to different user queries.</p></td></tr><tr><td align="center"><a href="skills">Skills</a></td><td><p>Skill is part of an agent that is specialized to understand and handle a specific task in the user conversation flow. </p><p></p><p><strong>Example</strong>: Order Pizza skill in a Pizza agent is responsible for taking the user through a conversation for capturing the required data to order a pizza.  </p></td></tr><tr><td align="center"><a href="intents">Intents</a> </td><td><p>An intent is an action in the user query that indicates what the user wishes to do. </p><p></p><p><strong>Example</strong>: Order Pizza, Find weather, Report claim.  </p></td></tr><tr><td align="center"><a href="intents">Training Data</a></td><td>For the agent to respond to user queries, each intent must be trained with specific sentences that are used as representative phrases for user queries. This set of sentences is referred to as the Training data set. Note that Training data are not user queries. Training data is a set of sentences that is used to train the intent for responding to user queries. </td></tr><tr><td align="center"><a href="../intents#training-data">User query</a></td><td>User queries are queries posted by the user to the agent. </td></tr><tr><td align="center"><a href="entity-types">Entities</a></td><td><p>If an intent refers to a verb or the action in the user query, then an entity refers to a noun (the object on which the action is performed) in the user query. </p><p></p><p><strong>Example</strong>: In the user query "I want to book tickets from San Francisco to New York", San Francisco and New York represent entities of type Location.</p></td></tr><tr><td align="center"><a href="../entity-types#entity-types">Entity type</a></td><td>An entity type is a named collection of similar objects such as states in a country, all pediatricians, a list of product names or data types (Date, Email, Location). Each entity type contains one or more values. </td></tr><tr><td align="center"><a href="slots">Slots</a></td><td><p>When information is extracted from a user sentence, the first task is to identify the intent behind the user query. The next task is to extract parameters in the training data that complete the intent. These are referred to as Slots. Each slot maps to an <a href="../entity-types#entity-types">entity type</a> for extraction and validation purposes.</p><p>  </p><p><strong>Example</strong>: In the user query "I want to book tickets from San Francisco to New York this Friday", San Francisco and New York are Slots that represent Location entity type and Friday represents DateTime entity type.</p></td></tr></tbody></table>