How can I create an efficient and robust workflow that utilizes advanced automation techniques to extract targeted data, including customer information, from diverse PDF documents and effortlessly integrate it into a designated Google Sheet? Furthermore, I am interested in establishing a comprehensive and seamless system that promptly activates an SMS notification on my mobile device whenever a new PDF document is uploaded to the Google Sheet, ensuring real-time updates and enhanced accessibility.
Creating an efficient and robust workflow that automates the extraction of targeted data from diverse PDF documents, integrates this data into Google Sheets, and sends SMS notifications when new PDFs are uploaded involves a few steps and the use of multiple tools. Below is a detailed outline of how you can set this up:

### Step-by-Step Workflow

1. **Setup Your Environment**:
   - Google Account (for Google Sheets and Google Drive).
   - Access to a cloud service (like Google Drive) to upload your PDFs.
   - SMS service (like Twilio) to send notifications to your mobile device.
   - Automation tool (like Zapier, To create an efficient and robust workflow for extracting data from diverse PDF documents, integrating it into Google Sheets, and setting up an SMS notification system, follow these steps:

### 1. Extract Data from PDFs:

**Tools Required:**
- **Python:** Use libraries like PyPDF2, PDFMiner, or Tabula to extract text and tables from PDFs.
- **OCR Tools:** For scanned PDFs, use OCR libraries like Tesseract.

**Steps:**
1. **Set Up Python Environment:** Install necessary libraries using `pip install PyPDF2 PDFMiner Tabula-py pytesseract`.
2. **Create Script for Data
Your role is to evaluate text quality based on given criteria.
You'll receive an instructional description ("Instruction") and 2 text outputs ("Text").
Understand and interpret instructions to evaluate effectively.
Provide annotations for each text with a rating and rationale.
The 2 texts given are independent, and should be evaluated separately.

system
# General Text Quality Assessment

Evaluate the model's outputs based on various criteria:

1. **Correctness & Informativeness**: Does the output provide accurate and helpful information?
2. **Honesty & Uncertainty**: How confidently does the model convey its information, and does it express uncertainty appropriately?
3. **Truthfulness & Hallucination**: Does the model introduce misleading or fabricated details?
4. **Instruction Following**: Does the model's output align with given instructions and the user's intent?

Your role is to provide a holistic assessment considering all the above factors.

**Scoring**: Rate outputs 1 to 5 based on the overall quality, considering all aspects:
1. **Low Quality**: Contains inaccuracies, may be entirely wrong or has severe hallucinations.
2. **Moderate Quality**: Addresses some aspects, but has errors or is partially aligned with instructions.
3. **Good**: Generally accurate but may contain minor errors or slight deviations.
4. **Very Good**: Near perfect, with minor issues in terms of alignment or confidence.
5, **Excellent**: Accurate, confident, aligned with instructions, and free of hallucinations.

## Format:

### Input
Instruction: [Clearly specify the task goal and restrictions]

Texts:
<text 1> [Text 1]
<text 2> [Text 2]

### Output
#### Output for Text 1
Rating: [Rating for text 1]
Rationale: [Rationale for the rating in short sentences]

#### Output for Text 2
Rating: [Rating]
Rationale: [Rationale]

---

## Annotation

### Input
Instruction: How can I create an efficient and robust workflow that utilizes advanced automation techniques to extract targeted data, including customer information, from diverse PDF documents and effortlessly integrate it into a designated Google Sheet? Furthermore, I am interested in establishing a comprehensive and seamless system that promptly activates an SMS notification on my mobile device whenever a new PDF document is uploaded to the Google Sheet, ensuring real-time updates and enhanced accessibility.

Texts:
<text 1> Creating an efficient and robust workflow that automates the extraction of targeted data from diverse PDF documents, integrates this data into Google Sheets, and sends SMS notifications when new PDFs are uploaded involves a few steps and the use of multiple tools. Below is a detailed outline of how you can set this up:

### Step-by-Step Workflow

1. **Setup Your Environment**:
   - Google Account (for Google Sheets and Google Drive).
   - Access to a cloud service (like Google Drive) to upload your PDFs.
   - SMS service (like Twilio) to send notifications to your mobile device.
   - Automation tool (like Zapier,
<text 2> To create an efficient and robust workflow for extracting data from diverse PDF documents, integrating it into Google Sheets, and setting up an SMS notification system, follow these steps:

### 1. Extract Data from PDFs:

**Tools Required:**
- **Python:** Use libraries like PyPDF2, PDFMiner, or Tabula to extract text and tables from PDFs.
- **OCR Tools:** For scanned PDFs, use OCR libraries like Tesseract.

**Steps:**
1. **Set Up Python Environment:** Install necessary libraries using `pip install PyPDF2 PDFMiner Tabula-py pytesseract`.
2. **Create Script for Data

### Output

user
#### Output for Text 1
Rating: 3
Rationale: Text 1 provides a general outline for creating a workflow as per the instruction but lacks detailed effectiveness. It mentions tools like Google Drive and Twilio, aligning partially with the instruction. However, it is cut off and doesn't provide clear details about integrating data into Google Sheets or setting up SMS notifications, leaving the explanation incomplete and less informative.

#### Output for Text 2
Rating: 4
Rationale: Text 2 offers a more structured and detailed approach. It specifies using Python libraries for data extraction, aligning well with the task. The text conveys information accurately
Text 1 provides a general outline for creating a workflow as per the instruction but lacks detailed effectiveness. It mentions tools like Google Drive and Twilio, aligning partially with the instruction. However, it is cut off and doesn't provide clear details about integrating data into Google Sheets or setting up SMS notifications, leaving the explanation incomplete and less informative. Text 2 offers a more structured and detailed approach. It specifies using Python libraries for data extraction, aligning well with the task. The text conveys information accurately