AI Path

This is Day 10 of the AI Path L1→L2 Upgrade Guide. You should complete Day 8 and Day 9 first. I learned this the hard way. “I thought I was being clear” is a lie I’ve told myself too many times while writing AI prompts. I once asked an AI to organize project documents: “Help me sort these files.” What I got back: all .md and .py files mixed together, sorted alphabetically by filename. It did sort them, just not the way I meant. Another time I said: “Show me the directory structure.” I wanted a tree view. The AI gave me ls -lh output: file sizes, timestamps, permissions, everything I didn’t ask for. The root cause wasn’t model capability. It was prompt specification. Refining your task description can mean the difference between three back-and-forth prompt iterations and getting it right on the first turn. Here are three exercises from everyday scenarios. Each starts with a vague version, breaks down what’s missing, and builds up to a clear version. If you read these and think “wait, I write prompts like the vague version too,” that is exactly why this article exists. ...

This is a bonus article for the “AI Path L1→L2 Upgrade Guide.” If you haven’t set up Codex yet, this guide gets you from zero to running. On July 9, 2026, OpenAI made two announcements at once: GPT-5.6 went public, and Codex was merged into the ChatGPT desktop app. The new desktop app has three modes: Chat, Work, and Codex, all in one app. If you already have the standalone Codex desktop app, it updates in place. Your projects and settings carry over. ...

This is a bonus article for the “AI Path L1→L2 Upgrade Guide.” If you have not installed OpenCode yet, this guide gets you from zero to running. On Day 8, you learned about autonomous execution AI. The next step is actually installing and configuring OpenCode. OpenCode is an open-source AI coding assistant. It is free, supports multiple models, and has a skill system. Paired with OpenCode Zen, OpenCode allows you to use tested models without setting up third-party API keys, including the free DeepSeek V4 Flash. ...

This is Day 9 of Week 2 in the “AI Path L1→L2 Upgrade Guide.” You should have completed Day 7 Exercise: Add Error Handling to Your Script first. Day 7 added error handling to your script, so it’s resilient now. But there’s a bigger cost factor you might have missed: the API provider you picked could cost a lot more than you think. DeepSeek V4-Pro charges $0.435 per million input tokens. OpenAI GPT-5.5 charges $5.00. That’s roughly an 11.5x difference. Factor in caching and the gap widens further. ...

This is Day 8 of Week 2 in the “AI Path L1→L2 Upgrade Guide.” You should have completed Day 7 first. On Day 7, you added error handling to your script. It now runs reliably in real network conditions. But there’s a more fundamental limitation: you still have to write code. Writing code to call APIs is one kind of automation. There’s a lighter one: describe the task, and let AI write the code, run it, and fix the bugs itself. That’s autonomous execution AI. ...

This is the Day 7 exercise for Week 2 of the “AI Path: Level Up Guide” series. Complete Day 6: Batch Processing Practice first. In Day 6, you wrote a batch processing script that works. Pick a scenario, walk the folder, call the API, save the results. It all runs smoothly until you try it for real. But that script runs in an idealized environment. Real networks are not ideal. Have you hit any of these with your script? ...

This is the Day 6 exercise for Week 2 of the “AI Path: Level Up Guide” series. Complete Day 5: Teach Your Script to Read More File Formats first, then come back here to work through Day 6. In Day 5: Teach Your Script to Read More File Formats, you built the read_file() function and the skeleton of a batch script. Your script recognizes files in various formats now. But you haven’t run a full pipeline from start to finish yet. You still need to pick a scenario, call the API, and save the results. That loop is missing. ...

This is Day 5 of Week 2 in the “AI Path L1→L2 Upgrade Guide” exercises. Read Part 2 first, then come back here. The batch_summarize.py script from Part 2 handles .md and .txt files. But real files come in many more formats. PDF reports, Word contracts, CSV data tables, JSON config files. They’re sitting on your desktop right now, and the script can’t touch them. Today’s goal: write a read_file() function that picks the right reader based on file extension, then plug it into the Part 2 batch script. ...

This is Part 2 of the “AI Path L1→L2 Upgrade Guide” series. Complete Part 1 and the first three days of exercises (Day 1, Day 2, Day 3) before continuing. Part 1 taught you to make one API call. Today you’re going bigger: make your program ask AI 100 questions. Manually pasting text into a chat window 100 times is grunt work. Writing a 10-minute script to automate the task is worth it. You get the time back. ...

This is the Day 3 companion exercise. Complete Day 1 first. Part 1 covers the theory (“Understanding API Parameters”). Today you verify it with your own eyes. Part 1 explained parameters in theory. But theory without practice is just noise. Today you run three experiments and see for yourself how parameters affect output. Setup Make sure your Day 1 project still works: uv run python hello_api.py If the AI replies, your environment is ready. All experiments below build on this code. ...