diff --git a/README.md b/README.md
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 - [Requirements](#requirements)
 - [Installation](#installation)
 - [Usage](#usage)
+- [Example: Textbook Q&A Generation](#example-textbook-qa-generation)
 - [Monitoring Dashboard](#monitoring-dashboard)
 - [Advanced Features](#advanced-features)
 - [Documentation](#documentation)
@@ -83,6 +84,10 @@ initial_data = RawData(data="Some raw data")
 graph.run(initial_tasks=[(data_processor, initial_data)])
 ```
 
+## Example: Textbook Q&A Generation
+
+PlanAI has been used to create a system for generating high-quality question and answer pairs from textbook content. This example demonstrates PlanAI's capability to manage complex, multi-step workflows involving AI-powered text processing and content generation. The application processes textbook content through a series of steps including text cleaning, relevance filtering, question generation and evaluation, and answer generation and selection. For a detailed walkthrough of this example, including code and explanation, please see the [examples/textbook](examples/textbook) directory. The resulting dataset, generated from "World History Since 1500: An Open and Free Textbook," is available in our [World History 1500 Q&A repository](https://github.com/provos/world-history-1500-qa), showcasing the practical application of PlanAI in educational content processing and dataset creation.
+
 ## Monitoring Dashboard
 
 PlanAI includes a built-in web-based monitoring dashboard that provides real-time insights into your graph execution. This feature can be enabled by setting `run_dashboard=True` when calling the `graph.run()` method.
diff --git a/examples/textbook/README.md b/examples/textbook/README.md
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+## Example: Textbook Question and Answer Generation
+
+PlanAI has been used to create a system for generating question and answer pairs from textbook content. This example demonstrates PlanAI's capabilities in processing educational material and automating complex workflows.
+
+### Project Overview
+
+The application processes textbook content to create question and answer pairs suitable for educational purposes or model training. It uses a series of AI-powered workers to:
+
+1. Clean and format text
+2. Identify relevant content
+3. Generate questions
+4. Evaluate question quality
+5. Generate and select answers
+
+The workflow is managed using the PlanAI framework, which allows for parallel processing of tasks while maintaining control over LLM API usage.
+
+### Key Components
+
+- **Text Cleaning (CleanText)**: Removes irrelevant content and improves text formatting.
+- **Relevance Filtering (InterestingText)**: Identifies text chunks suitable for Q&A generation.
+- **Question Generation (CreateQuestions)**: Produces multiple questions from each relevant text chunk.
+- **Question Evaluation (QuestionEvaluationWorker)**: Assesses and improves question quality.
+- **Answer Generation (QuestionAnswer)**: Creates multiple potential answers for each question.
+- **Answer Evaluation (AnswerEvaluator)**: Selects the best answer from the generated options.
+- **Output Handling (PrintOutput)**: Manages the final output of Q&A pairs.
+
+### Workflow
+
+1. The input text is divided into chunks and processed by the CleanText worker.
+2. InterestingText worker filters out irrelevant content.
+3. CreateQuestions generates multiple questions for each relevant chunk.
+4. QuestionEvaluationWorker assesses each question and suggests improvements if needed.
+5. QuestionAnswer generates two potential answers for each approved question.
+6. AnswerEvaluator selects the best answer based on accuracy and clarity.
+7. PrintOutput handles the final Q&A pairs, printing them and saving to a file.
+
+### Usage
+
+The application can be run from the command line, specifying the input file:
+
+```bash
+python textbook_app.py --file path/to/your/textbook.pdf