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RAG-based Tutoring Chatbot
A library in your pocket. A tutor by your side.
The RAG-based Tutoring Chatbot is made using Python, LangChain and FAISS. The system retrieves relevant context from a knowledge base before generating answers with Gemini Flash, grounding responses instead of relying on the model's raw knowledge.I evaluated it with the RAGAS framework, achieving ~0.87 faithfulness and ~0.83 answer relevancy, and deployed it live on Streamlit Community Cloud.I built this project from scratch with no prior coding experience, learning Git and GitHub along the way.
Top comment
This started as my NTCC project in college, but what really pulled me in was wanting to solve a real problem with LLMs — they can hallucinate or give outdated answers, which is risky for academic content that needs to be accurate and traceable. So I set out to build a tutoring chatbot that retrieves relevant context from a knowledge base before generating a response, grounding answers instead of just guessing plausibly. My approach evolved a lot along the way: I started with local sentence-transformer embeddings but hit RAM limits deploying on Streamlit Community Cloud's free tier, so I switched to the Gemini embeddings API; I had to pin my Python version after dependency conflicts, and debug a Gemini model deprecation mid-project. I also went from zero coding background to learning Git and GitHub through the browser UI, and eventually evaluating my system's output quality with RAGAS instead of just assuming it worked — which changed how I thought about "done."
About RAG-based Tutoring Chatbot on Product Hunt
“A library in your pocket. A tutor by your side.”
RAG-based Tutoring Chatbot was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #159 on the daily leaderboard. The RAG-based Tutoring Chatbot is made using Python, LangChain and FAISS. The system retrieves relevant context from a knowledge base before generating answers with Gemini Flash, grounding responses instead of relying on the model's raw knowledge.I evaluated it with the RAGAS framework, achieving ~0.87 faithfulness and ~0.83 answer relevancy, and deployed it live on Streamlit Community Cloud.I built this project from scratch with no prior coding experience, learning Git and GitHub along the way.
On the analytics side, RAG-based Tutoring Chatbot competes within API, Education, GitHub and Bots — topics that collectively have 330.2k followers on Product Hunt. The dashboard above tracks how RAG-based Tutoring Chatbot performed against the three products that launched closest to it on the same day.
Who hunted RAG-based Tutoring Chatbot ?
RAG-based Tutoring Chatbot was hunted by Aksa Fatima. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of RAG-based Tutoring Chatbot including community comment highlights and product details, visit the product overview.