> ## Documentation Index
> Fetch the complete documentation index at: https://docs.re-factor.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Your Re-Factor TL;DR

<img src="https://mintcdn.com/pscilabs/MyP0jeqcDcoyhmPz/images/purplecircuitchina.png?fit=max&auto=format&n=MyP0jeqcDcoyhmPz&q=85&s=86faf86c8a3c07cebeacd7d98f18d1ca" alt="Purple China" width="1792" height="1024" data-path="images/purplecircuitchina.png" />

## Key Components

Re-Factor is designed to enable business teams, data scientists, and engineers robustly design, evaluate, and subsequently implement AI within their business processes. We have a few core concepts that will help you make best use of the platform.

| Component                                   | Description                                                                                                                                                                      | Key Features                                      |
| ------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------- |
| [Resource](/guides/resources)               | Our general term for any piece of unstructured or structured data your AI-driven processes will utilize. This can include documents of any type, webpages, emails, and more.     | Resource Repository                               |
| [LLM](/guides/llms)                         | A LLM is an AI model that is capable of generating text based on a prompt or context. LLMs are used in various forms of AI-driven business processes.                            | Integrations with LLM Providers                   |
| [Prompt](/guides/prompts)                   | A prompt is the vehicle for delivering context, instructions, and embedded resources to a large language model.                                                                  | Runnable Studio                                   |
| [Tool](/guides/runnables/tools)             | A tool is an interface to an external system. Tools provide a means for your LLM to access external resources, enabling LLMs to go beyond generating text and perform real work. | Tool Registry, Tool Designer                      |
| [Completion](/guides/runnables/completions) | A completion is the basic unit of response from an LLM and can be used to generate text or structured data.                                                                      | Runnable Studio, Runnable API, Runnable Dashboard |
| [Flow](/guides/runnables/flows)             | A flow is a workflow that executes a series of tasks in the form of prompts and tool calls. Flows give you a way to exert finer control over sensitive or costly processes.      | Runnable Studio, Runnable API, Runnable Dashboard |
| [Agent](/guides/runnables/agents)           | An agent is a highly intelligent AI that is capable of choosing in real time the prompts and tools to use to achieve a desired outcome.                                          | Runnable Studio, Runnable API, Runnable Dashboard |
| [Evaluation](/guides/evaluations)           | An evaluation is an assessment of specific characteristics of the outputs of your AI-driven actions in your processes.                                                           | Laboratory, Experiment Log                        |
| [Experiment](/guides/experiments)           | An experiment is a vehicle for comparing the performance of different configurations of a prompt, flow, or agent with one another in order to optimize their performance.        | Laboratory, Experiment Log                        |

## Using re-factor

We've been building AI-enhanced business processes for over a decade. In the LLM era, we AI-driven business process automation has one key challenge: the studio environments for building are unproductive and hard to use. So we designed re-factor to solve everything broken about the current paradigm.

Our workflow:&#x20;

1. **Craft** a prompts, flows, and/or agents in the Re-Factor Workbench

2. **Experiment** with different instructions, models, parameters, and more to achieve the lowest latency and cost with the highest accuracy for your workflow.

3. **Deploy** your prompts, flows, and agents to into productive use via our SDK or API.

4. **Observe** the behavior of your prompts, flows, and agents in production to ensure accuracy and cost-efficiency.

5. **Iterate** on your prompts, flows, and agents to continuously improve accuracy and cost-efficiency with assistance from re-factor AI.

***

## Why choose re-factor

We find Re-Factor is best for teams that have a few key goals while developing their AI business processes.

1. You want total control over every aspect of your prompts, model selection, tool registry, and more.

2. You need robust evaluations, versioning, and deployment slots to ensure accuracy in production and continuous improvement.

3. You want a dedicated design tool that enables you to iterate far faster to get to production.

***

<CardGroup cols={2}>
  <Card title="API Documentation" icon="pen-to-square" href="https://docs.re-factor.ai/api-reference">
    View our API Reference to learn how to build with re-factor
  </Card>

  <Card title="Join the Community" icon="image" href="https://discord.gg/wmP5JRQr7t">
    Find us on Discord to connect with other builders, get help, and give feedback.
  </Card>
</CardGroup>
