How I Made My RAG Bot Understand What You Actually Mean

Ever built something that technically works but practically flops when it really matters? Yeah, that was me — trying to make a smart RAG chatbot… and realizing it didn’t always “get” what users were really asking.

🤔 The Problem

I was working on a RAG-based assistant — standard stuff:
🔹 User asks a question
🔹 We embed it,
🔹 Retrieve chunks,
🔹 Feed it to the LLM

for a response.

It was decent, until I tested it with slightly vague or ambiguous questions.

Like imagine someone asks:
“What is FS?”
And boom — it gives a long explanation of the fs module in Node.js.

But what if the user actually meant:

• “What is the fs module used for?”

• “How do I handle errors with fs?”

• “What are the dependencies or alternatives?”

• “What even is a module?”

The bot had no idea. It just took the prompt literally.

🔍 What I Tried (and Loved)

So I did what any curious dev would do: went into research rabbit holes for a weekend. Found some really cool concepts — and implemented them.

🧪 1. Prompt Expansion: Think Like the User

Instead of sticking to what the user typed, I started expanding the question behind the scenes.

If the input is:

“What is fs?”

I auto-generate 4–5 related interpretations like:

• “What is the fs module in Node.js?”

• “What are common issues with fs?”

• “What’s the use-case of fs?”

• “Alternatives to fs module?”

• “What is a module in programming?”

Then I retrieve docs for each of these, embed them, and...

⚖️ 2. LLM-as-a-Judge: Let AI Pick the Smartest Interpretation

Now comes the fun part:
I let the LLM itself decide which one of these interpretations best matches the user’s intent.

Basically, the LLM becomes a judge, not just a responder.

It looks at all the retrieved chunks and picks the most relevant one to answer from. And boom — way better accuracy, even for fuzzy questions.

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📚 What I Learned

This whole experience taught me something big:

Sometimes the fix isn’t better data or a new model…
It’s just thinking more like the user.

I’m still learning, but this small change made a huge difference in how useful the chatbot felt.

If you’re working on RAG or any question-answer system — try this. It’s simple, but powerful.

Happy to chat if anyone’s trying something similar! 💬