Building a Readability Evaluator in FlowHunt
Learn to build a readability evaluator in FlowHunt using popular metrics like Flesch-Kincaid. Follow a step-by-step guide to enhance your content quality and engage readers.

With the web being flooded by poorly generated and equally poorly written content, keeping your readability in check is the key to keeping readers hooked on your content. Funnily enough, AI can help you with evaluating readability. Here’s how.
In this video, you’ll learn how to create a readability evaluator that supports all the popular readability metrics. Prefer reading over watching? No problem—you can read everything we’ll cover in the video right here in this blog post.
Creating a Readability Evaluator: A Step-By-Step Guide
This guide will walk you through the process of building a robust readability evaluator using FlowHunt. With all the proven metrics ready to go, this tool will help you better understand the quality of your content.
Step 1: Setting Up The Flow
Begin by navigating to the “My Flows” tab and creating a new flow. Name it appropriately and add a description to keep your projects organized. You’ll find yourself with a blank canvas, ready to build.

Step 2: Essential Components
Let’s start with the foundation of any flow:
- Input: This is where the user’s query (the text to be analyzed) is entered.
- Output: This component displays the chatbot’s response, which will be your readability analysis.

Adding the Readability Evaluator Component
The heart of this Flow is the readability evaluator component. This powerful tool is designed to analyze any text input, providing a comprehensive assessment without additional dependencies. Connect this component between your input and output to process the text and generate results.

The readability evaluator utilizes most of the industry-standard readability metrics to provide a well-rounded analysis, including:
- Flesch-Kincaid
- Dale-Chall
- Flesch Reading Ease
- ARI (Automated Readability Index)
- Coleman-Liau
- Gunning Fog
- SMOG
- Spache
- Linsear Write
These metrics offer insights into text complexity, grade level, and overall accessibility, allowing you to tailor your writing to your intended audience.
Step 5: Finalizing Your Flow
Connect the readability evaluator to the output component to display the detailed breakdown of readability scores and actionable insights for optimizing your writing.
That’s all you need to do! By following this simple guide, you’ve created a great tool for any writer. Want to take the next step? Try our AI Rewriter to fix any readability issues.
Frequently asked questions
- What is a readability evaluator?
A readability evaluator is a tool that analyzes text using industry-standard metrics to determine its complexity, grade level, and accessibility for readers.
- Which readability metrics are supported in FlowHunt?
FlowHunt's readability evaluator supports metrics including Flesch-Kincaid, Dale-Chall, Flesch Reading Ease, ARI, Coleman-Liau, Gunning Fog, SMOG, Spache, and Linsear Write.
- Who can benefit from using a readability evaluator?
Content creators, marketers, educators, and anyone aiming to improve the clarity and accessibility of their writing can benefit from using a readability evaluator.
- Do I need coding skills to build a readability evaluator in FlowHunt?
No coding skills are required. FlowHunt provides a no-code platform with intuitive components to build readability evaluators and other AI tools easily.
Try FlowHunt's Readability Tools
Discover how FlowHunt's AI-powered readability evaluator can help you optimize your content for clarity and engagement.