Amazon Lex V2 core concepts

Bot – A bot performs automated tasks such as ordering a pizza, booking a hotel, ordering flowers, and so on. An Amazon Lex V2 bot is powered by automatic speech recognition (ASR) and natural language understanding (NLU) capabilities.

Amazon Lex V2 bots can understand user input provided with text or speech and converse natural language.

Language – An Amazon Lex V2 bot can converse in one or more languages. Each language is independent of the others, you can configure Amazon Lex V2 to converse with a user using native words and phrases. For more information, see Languages and locales supported by Amazon Lex V2.

Intent – An intent represents an action that the user wants to perform. You create a bot to support one or more related intents. For example, you might create an intent that orders pizzas and drinks. For each intent, you provide the following required information:

In addition to custom intents, Amazon Lex V2 provides built-in intents to quickly set up your bot. For more information, see Built-in intents.

Amazon Lex always includes a fallback intent for each bot. The fallback intent is used whenever Amazon Lex can't deduce the user's intent. For more information, see AMAZON.FallbackIntent.

Slot – An intent can require zero or more slots, or parameters. You add slots as part of the intent configuration. At runtime, Amazon Lex V2 prompts the user for specific slot values. The user must provide values for all required slots before Amazon Lex V2 can fulfill the intent.

For example the OrderPizza intent requires slots such as size, crust type, and number of pizzas. For each slot, you provide the slot type and one or more prompts that Amazon Lex V2 sends to the client to elicit values from the user. A user can reply with a slot value that contains additional words, such as "large pizza please" or "let's stick with small." Amazon Lex V2 still understands the slot value.

Slot type – Each slot has a type. You can create your own slot type, or you can use built-in slot types. For example, you might create and use the following slot types for the OrderPizza intent:

Amazon Lex V2 also provides built-in slot types. For example, AMAZON.Number is a built-in slot type that you can use for the number of pizzas ordered. For more information, see Built-in intents.

Version – A version is a numbered snapshot of your work that you can publish for use in different parts of your workflow, such as development, beta deployment, and production. Once you create a version, you can use a bot as it existed when the version was made. After you create a version, it stays the same while you continue to work on your application.

Alias – An alias is a pointer to a specific version of a bot. With an alias, you can update the version the your client applications are using. For example, you can point an alias to version 1 of your bot. When you are ready to update the bot, you publish version 2 and change the alias to point to the new version. Because your applications use the alias instead of a specific version, all of your clients get the new functionality without needing to be updated.

Assisted NLU – Uses Large Language Models (LLMs) to improve intent classification and slot resolution. This feature helps your bot understand user requests more accurately, even when they use different phrasing than your training examples. Assisted NLU works within your configured intents and slots, providing better understanding without requiring extensive training data.

Multi-turn Conversations – Amazon Lex V2 can maintain context across multiple conversation turns, allowing for natural back-and-forth interactions. Users can provide information gradually, change their mind, or ask clarifying questions without losing the conversation context.

Context Switching – Advanced bots can handle topic changes within a conversation. For example, a user might start asking about account information, then switch to placing an order, and return to the original topic. Amazon Lex V2 can manage these context switches gracefully.

Fallback Strategies – When Amazon Lex V2 doesn't understand a user's request, you can configure sophisticated fallback behaviors including clarifying questions, suggestion prompts, or escalation to human agents. This ensures users always have a path forward in the conversation.

Conversation Flow Management – Use conditional branching and conversation flow controls to create complex dialog patterns without writing code. You can route conversations based on user responses, slot values, or external data.