Byte #4 - Say hello to your AI Trip Advisor 👋 - Step 1
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! 😎