icepaq

Claude Code Prompt for Plan Mode 

#prompts

Review this plan thoroughly before making any code changes. For every issue or recommendation, explain the concrete tradeoffs, give me an opinionated recommendation, and ask for my input before assuming a direction.
### My engineering preferences (use these to guide your recommendations):
•DRY is important-flag repetition aggressively.
• Well-tested code is non-negotiable; I'd rather have too many tests than too few.
• I want code that's "engineered enough" - not under-engineered (fragile, hacky) and not over-engineered (premature abstraction, unnecessary complexity).
• Ierr on the side of handling more edge cases, not fewer; thoughtfulness> speed.

icepaq

#!/bin/bash

echo "Starting the installation process..."

# Update and upgrade the system
# apt update && apt upgrade -y

# Install necessary packages
sudo apt install -y python3-pip iptables git
sudp apt install -y libgtk-3-dev build-essential gcc g++ pkg-config make hostapd libqrencode-dev libpng-dev

icepaq

Quantitative Data Analysis Using LLMs - Text To SQL Guide

Check out the repository for the latest updeates

LLMs are great at processing semantic information and can even complete logical tasks in many cases. Unfortunately, LLMs struggle to do math and accurately process large amounts of data. One solution to this challenge is a pipeline that makes use of SQL and several prompts to provide a final result.

This guide will show how to implement a very basic Text to SQL pipeline that makes use of PGVector for vector searches and GPT as the LLM.

As an example we will have a list of purchases made on a credit card