📊 Data Gathering: First, we collect information about the patient. This includes their medical history, genetic makeup, and lifestyle choices. Think of it as gathering ingredients for a magical potion.
🤖 AI Analysis: Our trusty AI wizards step in! They analyze the data faster than a lightning bolt. They look for patterns, hidden clues, and even predict future health issues. It’s like having a crystal ball for medicine.
🎯 Personalized Treatment Plan: The AI elves create a personalized treatment plan. For Mrs. Thompson, who loves gardening, they mix in herbal remedies. And for Mr. Johnson, the marathon runner, they add an energy-boosting spell. Each patient gets their unique potion! 🌿🏃♂️
🧪 Testing and Monitoring: We test the treatment plan like alchemists in a lab. If it works, great! If not, back to the drawing board. And as time goes by, we adjust the potion based on how the patient responds.
☕ Coffee Break: Dr. Amelia sips her coffee while the AI does the heavy lifting. No more stacks of paperwork—just magic and science! ☕✨
When working with large CSV files and querying them using a Language Model (LLM), optimizing your approach to minimize token usage is crucial. This helps reduce costs, improve performance, and make your system more efficient. Here’s a beginner-friendly guide to help you understand how to achieve this. What Are Tokens, and Why Do They Matter? Tokens are the building blocks of text that LLMs process. A single word like "cat" or punctuation like "." counts as a token. Longer texts mean more tokens, which can lead to higher costs and slower query responses. By optimizing how you query CSV data, you can significantly reduce token usage. Key Strategies to Optimize LLM Queries for CSV Files 1. Preprocess and Filter Data Before sending data to the LLM, filter and preprocess it to retrieve only the relevant rows and columns. This minimizes the size of the input text. How to Do It: Use Python or database tools to preprocess the CSV file. Filter for only the rows an...
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