🧠 Evaluating Chatbots with GenAI : The Problem, The Potential, and The Plan

#ai #machinelearning #programming #devchallenge

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.