Chatbots are evolving fast. Evaluating them? Not so much.
5-Day Generative AI Intensive course by Google & Kaggle, where we explored how to apply cutting-edge GenAI tools in real-world projects. At the end of the course, we were challenged to build a capstone project around one big question:
🧩 How can we use GenAI to solve a problem that traditionally required human effort, manual work, or complex logic?
So I zeroed in on one of the trickiest challenges in the LLM space:
How do you evaluate chatbot responses without human reviewers?
The Problem with Evaluating LLMs 🤖
Imagine building a chatbot. It talks. It answers. It vibes. But… how do you know it’s good?
You can’t just count matching words. That’s like rating a movie by checking if it includes the word “explosion.”
Asking humans to evaluate hundreds of responses? Painful, slow, inconsistent, and not scalable.
And yet, evaluation is critical. If you’re:
Comparing multiple LLMs (GPT-4 vs Claude vs Mistral)
Fine-tuning models on your own data
Shipping AI chat features in your product
…you need to know which outputs suck and why— fast.
Enter GenAI Evaluation: LLMs Judging LLMs 🧠
Here’s where things get spicy.
Instead of manually evaluating responses, what if we could get an LLM to do it for us?
That’s the core idea of my capstone:
Use a GenAI model (Gemini 2.0 Flash) to rate chatbot responses on key quality metrics.
This isn’t just automating a task — it’s using intelligence to evaluate other intelligence. Wild, right?
Capstone Project Requirements 📋
The project had to meet a few key criteria:
| Requirement ✅ | Our Approach 💡 |
|---|---|
| Use real-world data | We used the OpenAssistant Dataset (OASST1), a huge collection of human-assistant conversations. |
| Solve a practical problem | We tackled the LLM evaluation bottleneck, a major issue in GenAI dev workflows. |
| Leverage GenAI capabilities | We used Gemini 2.0 Flash to generate scores on relevance, helpfulness, clarity, and factuality. |
| Automate a previously manual process | We created a fully autonomous pipeline for evaluating chatbot responses. |
This wasn’t just for fun — it had real applications, and could be extended into production tools.
GenAI Capabilities We Used ⚙️
Here’s what made this project tick:
Few-shot prompting
We added scoring examples to the prompt so the model understood the rating scale. Like teaching a mini-AI to become a harsh movie critic.Structured Output (JSON)
Instead of vague “This looks good” answers, Gemini returned proper JSON like:
{
"relevance": 4,
"helpfulness": 5,
"clarity": 4,
"factuality": 3
}Machine-readable. Developer-friendly 🤌🏻 .
- Gen AI evaluation Used Gemini to auto-evaluate chatbot responses on multiple dimensions.
Real-World Use Cases 🔥
This approach isn’t just for academic flexing — it’s legit useful for:
✅ Startup teams testing new AI chat features
✅ Researchers comparing open-source LLMs
✅ Devs fine-tuning models on their own datasets
✅ QA pipelines for chatbot apps
And the best part? It scales like crazy. Want to evaluate 1,000 responses overnight? Just batch it and go.
What’s Next ?? 👀
Now that we’ve covered the “why” — get ready for the “how.”
In Next Post, I’ll walk you through:
Setting up the dataset (OASST1)
Extracting prompt-response pairs
Prompting Gemini to score responses
Parsing and analyzing the results
Visualizing it all with plots and metrics
If you’re into GenAI, data science, or just building cool stuff — you’re gonna love it.
📌 TL;DR
Evaluating LLMs manually is slow, messy, and subjective. So I built an auto-eval system using Google’s Gemini to score chatbot responses on relevance, clarity, helpfulness, and factuality — no humans needed. Part 2 drops soon with all the nerdy build details.
