Byte #4 — Say hello to your AI Trip Advisor 👋 — Step 1

Sep 09, 2025

Welcome back to HosseiNotes — your weekly Byte of simplified AI engineering, with a side of fun 🚀😅

Last week, we saw how clean prompts scale like clean code. This week, let’s start building something fun together: a Mini Trip Advisor GPT.

🛑 The Problem

Ask a vanilla LLM: “Plan me a trip this weekend.” It happily spits out:

✈️ “Fly to Paris. Visit the Louvre. Michelin-star dinner.”

Sounds great, until you realize:

You live in Tokyo 🗼
It’s raining 🌧️
Your budget = ramen 🍜

Classic AI move → confident, wrong, and totally out of context (Though to be fair, newer models are getting better and better at this 😎).

🛠️ Step 1 — Building the MVP

We’ll fix this by teaching our GPT to ask questions one by one and use each answer to refine the final recommendation.

Here’s the protocol:

🧾 The Prompt (Step-by-Step Questions + Instructions)

You are a helpful travel advisor. 
Your job is to recommend a fun trip plan. 
Before giving a recommendation, always follow these steps:

Step 1: Ask the user where they are starting from. 
       → Instruction: ensure the final recommendation is realistically close to this starting point. 

Step 2: Ask the user how long they want the trip to be. 
       → Instruction: consider only destinations and activities that fit this timeframe. 

Step 3: Ask the user what budget they have. 
       → Instruction: recommend options that fit their budget. 

Step 4: Ask the user what type of experience they prefer (outdoors, culture, lazy beach). 
       → Instruction: adapt your suggestions to match their preference. 

Step 5: Once you have all of this information, reason through the answers step by step 
        and then provide a tailored trip recommendation.

👉 Copy-paste this into ChatGPT → congrats, you’ve just built your MVP Trip Advisor GPT 🎉

✅ What You’ve Learned

By forcing the model to:

Ask step by step
Tie each answer to a specific instruction
Only answer after reasoning through all steps

…you’ve actually used a technique researchers call Chain of Thought (CoT) prompting.

🧪 Scientific definition: Chain of Thought prompting guides large language models to explicitly perform intermediate reasoning steps before producing the final answer — improving accuracy, consistency, and interpretability (Wei et al., 2022).

So, congrats 🎉 — you just learned Chain of Thought in action!!

TL;DR

Vanilla LLMs = terrible trip planners 🫠
Step-by-step questioning = smarter, context-aware AI ✨
Congrats: you’ve just mastered Chain of Thought prompting in practice 🎓

🚀 See you next week with a new Byte — and the next step of our Trip Advisor GPT! 😎

HosseiNotes