# Build your first agent in just a few minutes with [Rasa Copilot](https://hello.rasa.com/?utm_source=docs&utm_medium=referral&utm_campaign=docs_cta).

This guide is intended as an introduction to how to create conversations that are helpful and feel natural.

It's designed specifically with **designers** in mind, but it's also useful for anyone who wants to learn how to get the most out of Rasa.

## Topics covered

- What makes a good conversation? CxD
- How CxD works with CALM
- How to write responses that feel human
- How to test and improve through feedback

## What is Conversation Design (CxD)?

**Conversation Design** is the process of researching, conceptualizing, and creating conversational interactions between human users and AI. It is an interdisciplinary field drawing from UX design, linguistics, conversation analysis, content management, human-computer interaction, and related studies.

Conversation design is both human-centered and data-driven.

## What is CALM?

**Rasa CALM** is a state-of-the-art hybrid approach to building conversational AI assistants. It combines 'flows' — predefined sets of steps to complete tasks that represent business processes — with the power of language models to:

- Understand user intentions
- Handle edge cases
- Repair conversations
- Generate responses where appropriate

To read more about CALM, see the [CALM documentation](/content/docs/learn/concepts/calm/index.html).

## How does CALM affect the CxD process?

As a conversation designer, each project starts with **discovery**, focusing on the use cases, scope, and tasks for the AI assistant. Extensive research is conducted on:

- Target audience interaction goals and needs
- Conversation habits
- Language styles

### Outcomes of the Discovery Phase:

The research leads to user journeys that are converted into _conversational flows_ — dynamic representations of possible conversation paths between the user and AI assistant. These flows execute **Business Logic** in CALM.

### Designing with CALM:

You don't need complex flowcharts with many interlinked branches. Instead, each flow can focus on specific tasks, such as:

- Responding to user questions
- Connecting to a knowledge base for requested information
- Collecting user information in a series of steps
- Performing actions (e.g., checking a balance, blocking a card, booking an appointment)
- Transferring to a human operator

### Building a Flow in CALM:

While building flows in CALM, you can predefine:

- The assistant's responses (which could also be generated)
- Information to collect from the user ( _slots_) and subsequent actions
- How to act based on user inputs ( _logic_)
- Next steps in the conversation ( _links_)

CALM's **Dialogue Understanding** module leverages language models to interpret user statements and intentions. As a designer:

- You aren't required to build sets of intents, entities, and variations for every scenario.
- However, you can still do so for specific scenarios if desired.

### LLM-Based `CommandGenerator`:

The `CommandGenerator` translates user input into commands that drive the conversation forward by triggering flows, operations, repair patterns, and more. It considers:

- Conversation history
- Context

#### Key Customization Options:

- **Prompting the LLM:** Use flow descriptions and slot definitions to guide the LLM in generating appropriate commands.
  [Learn more about prompting](/content/docs/reference/config/components/llm-command-generators/#customization/index.html).
- **Flow Retrieval:** Pre-select relevant flows for a given conversation.
  [Learn more about flow retrieval](/content/docs/reference/config/components/llm-command-generators/#retrieving-relevant-flows/index.html).

## CxD Workflow in CALM

### Outside of Rasa: Designer Responsibilities

- Gather data on user needs, language style, and conversational habits
- Define user personas, map content, and user journeys
- Create an AI assistant personality
- Write sample dialogues
- Draft conversational flows
- Plan error handling, escalation strategies, and handovers (if needed)
- Prepare user testing rounds and protocols

### Inside Rasa: Builder Responsibilities

- Build flows based on designs and user journeys
- Write efficient flow and slot descriptions to guide the LLM
- Create responses (if needed) that align with personality guidelines
- Prompt the LLM to generate or rephrase responses (if needed)
- Customize conversation repair patterns per error handling strategies
- Write end-to-end (e2e) tests based on sample dialogues
- Connect the assistant to knowledge sources and implement RAG
- Instruct the LLM on generating texts for RAG responses
- Debug designs and test the assistant using Rasa Inspector