In today’s blog post, I’d like to delve deeper into the nuances of extracting similar variables with Datavestigo.

As we all know, clear and precise prompts are the magic behind the effectiveness of all AI-driven models. The same principle applies to Datavestigo, which relies heavily on AI for accurate data extraction. This means that the way you define the desired data can make a significant difference in minimizing mistakes and achieving flawless results.

During a project with a customer who was setting up Datavestigo for competitive analysis, we discovered an intriguing insight. Datavestigo produced more accurate and error-free results when it was properly configured. How did we achieve this? Continue reading to find out!

Our customer aimed to extract multiple data points: the price with VAT, the product name, the URL address, and the product description, all into an Excel table. Initially, we encountered issues where Datavestigo confused the price with VAT for the price without VAT at several competitor e-shops. The primary cause was that the AI struggled to differentiate between these two similar values due to an unclear prompt.

The solution to this problem is straightforward: extract both values. Specifically, in this case, instruct Datavestigo to extract both the price with VAT and the price without VAT separately. By clearly defining both fields, we ensured that the AI understood exactly what to look for in the documents. When we implemented this modified prompt, Datavestigo no longer confused the two values. The results were accurate and free from errors.

Here’s a step-by-step breakdown on how to achieve these more accurate results:

  1. Identify Similar Variables: Recognize which variables in your document or dataset are similar and prone to confusion by the AI. In our case, it was the price with VAT and the price without VAT.
  2. Define Each Variable Clearly: When setting up your Datavestigo prompts, make sure to clearly define each variable you want to extract. Instead of asking for just the ‘price,’ specify ‘price with VAT’ and ‘price without VAT.’
  3. Test with Multiple Prompts: Run several test scenarios with different prompt formulations. This helps to identify the most effective way to communicate your needs to the AI.
  4. Review and Adjust: After each test, review the extracted data for accuracy and make necessary adjustments to your prompts. This iterative process will refine the extraction rules to minimize errors.

By following these best practices, we were able to improve DataVestigo’s accuracy significantly. Here’s an illustrative example:

Before:

  • Defined values to be extracted: the price with VAT, the product name, the URL address, and the product description
  • Result: AI returns the price but sometimes confused whether it included VAT or not.

After:

  • Defined values to be extracted: the price with VAT, the price without VAT, the product name, the URL address, and the product description
  • Result: AI returns both prices accurately, distinguishing between them clearly. In conclusion, if you need to extract two similar values from a document or web source, always define both fields and explicitly request both sets of data. This approach ensures that Datavestigo delivers more accurate results with fewer mistakes. By doing so, you’ll be leveraging the full capabilities of Datavestigo for precise data extraction, ultimately enhancing the quality of your competitive analysis or any other task requiring detailed data retrieval. Keep these tips in mind, and happy extracting with DataVestigo!