Byte #5 — Your AI Trip Advisor 👋 — Step 2: Tool Superpowers 🦸♂️
Welcome back to HosseiNotes — our growing hub for building atomic learning habits in AI engineering (with a side of fun 😅).
Last week we built a Mini “Trip Advisor GPT.” It learned to ask questions step by step before making a plan. Smarter than vanilla ChatGPT… but still a dreamer.
Why? Because it had no idea about:
🌦️ The weather (no hiking in a thunderstorm! ⚡️)
✈️ Real prices (Paris on €50?!… sure 💸)
🏨 Hotel costs (not everyone wants to “sleep under the stars” 🛏️)
This week, let’s fix that. We’ll give our GPT superpowers by letting it use ChatGPT’s browsing tool to check the real world before answering. (No capes required 🦸♂️).
🛑 The Problem
Ask your MVP Trip Advisor GPT:
“Plan me a trip from Madrid this weekend, budget €300.”
It may say:
✈️ “Fly to Paris. Visit the Louvre.”
Nice flex… until you check:
🌧️ Paris = rainy weekend,
💸 Train tickets = €400.
Classic LLM move → confident, wrong, and totally out of touch.
🛠️ Step 2 — Teaching It to Use Tools
Instead of guessing the weather or hotel prices, we tell our GPT:
👉 “Use your browsing tool to check the weather, transport prices, and hotel costs before deciding.”
That one extra line changes everything.
🧩 The Secret Sauce: Tool Use
Here’s the new piece you need:
Use your browsing tool to fetch real data for the target dates:
1. Check the weather forecast for the travel dates for the starting city and 2–4 nearby/candidate destinations.
2. Check typical transport prices (flight or train) from the starting city to each candidate destination for those dates.
3. Check average hotel prices per night at each candidate destination (pick a reasonable category for the stated budget).We also tell GPT how to filter what it finds so the plan stays realistic:
Filter candidates:
- Remove options that don’t fit the weather preference (e.g., rainy for outdoors).
- Remove options that don’t fit the trip duration (too far for short trips).
- Remove options where travel + hotel cost would exceed budget (give a quick reasoning). 👉 That’s the magic: browsing brings in real numbers, filtering applies them — and suddenly your GPT is reality-aware. 😎
🕵️♀️ Behind the Scenes
When you say “use your browsing tool,” and it has browsing enabled, here’s what’s actually happening:
1️⃣ It parses your request and goes “Okay, I need weather and prices.” 🤔
2️⃣ It types real queries into the web (like “Madrid to Valencia train price September 20”). 🔎
3️⃣ It reads snippets from web pages (often travel sites, weather.com, booking.com, etc.). 📚
4️⃣ It brings the results back to the chat 💼
5️⃣ Then it reasons step by step 🧠 (thanks to Chain of Thought instructions) and gives you an answer that’s grounded in reality 🌍
Magic? Kinda. More like: 🗣️ prompt → 🔎 search → 📖 read → 🧠 reason → ✅ answer
🎉 Test It!
👤 User: “Plan me a 2-day trip. I’m in Madrid, budget €300, prefer outdoors.”
🤖 ChatGPT (with browsing):
Finds Valencia sunny 🌞, Bilbao rainy 🌧️
Sees roundtrip train to Valencia ~€60 🚆
Finds hotels ~€80/night 🏨
Total ~€220 = ✅ fits budget
👉 Suggests Valencia: beach walk, hiking, tapas tour.
✅ What You’ve Learned
LLMs sound smart but often make things up (hallucinations).
Adding step-by-step reasoning makes them more reliable.
Adding tool use makes them reality-aware (weather, prices, hotels).
Together = an AI that’s not just a dreamer, but a doer.
TL;DR
Vanilla LLMs = fantasy trip planners 🫠
Step-by-step reasoning = structured thinking 🧠
Tools = real-world awareness 🌍
Congrats 🎉 → you’ve just learned the magic of tool-augmented prompting.
🚀 See you next week with a new Byte — and the next step of our Trip Advisor GPT! 😎