How to automatically generate reports with DeepSeek's AI software?

I. Core principles of the DeepSeek report generation function

DeepSeek's AI report generation function is based on natural language processing (NLP) and machine learning technology, and automatically completes the three core aspects of information integration, data analysis, and text generation by analyzing the data sources and instruction requirements provided by users. The system is equipped with intelligent semantic comprehension capabilities, and can recognize input content in various formats such as Excel tables, databases, web data, etc., and generate fully structured documents based on preset templates or user-defined requirements.

II. Demonstration of standard operating procedures

1. Data preparation phase
   • Organize sales data into a standardized Excel spreadsheet (need to include fields such as date, product category, sales, etc.)
   • Upload market research PDF report to system document library
   • Check the "Year-on-year analysis" and "Trend forecasting" options in the parameter settings.
2. Template Selection and Configuration
   • Select the "Monthly Marketing Report" template from the template library.
   • Set timeframe to Q2 2023
   • Adjust the visualization chart type to a combination of line + bar charts
3. Generation and Optimization
   • Click on the "Intelligent Generation" button to initiate AI processing (average time 2-5 minutes)
   • Adjusting content through natural language instructions: "Please enhance the competitor analysis section"
   • Modify the data labeling format using the online editor

III. Skills for advanced use

IV. Solutions to common problems

• Data recognition anomalies: Check whether the form field contains special characters, it is recommended to use "Sales_Million" and other standardized naming methods.
• Content Logic Error: Add exclusions to prompts, e.g., "Ignore test data, analyze only formal environmental records".
• formatting confusion: Use Markdown syntax to label highlights, e.g. ## Core Conclusion ##

V. Best practice cases

An e-commerce company automates its operational daily reports with the following configuration:

1. Automatic synchronization of order data in MySQL database at dawn every day
2. Set up early warning rules for key metrics (e.g. returns > 5% marked red to show)
3. Bind enterprise WeChat push channel and automatically send to management group at 8:00 daily

Manual processing time was reduced from 3 hours/day to 15 minutes/day after implementation, and data accuracy was improved by 40%.

VI. Security considerations

• Localized deployment version recommended for sensitive data
• Enable Document Version Control
• Regular review of AI-generated proposal content