RAG
Answerly
An AI-powered Q&A system for e-learning platforms that delivers instant, accurate answers using RAG, FAISS semantic search, and HuggingFace embeddings over a structured knowledge base.
Tech Stack
Python
Streamlit
LangChain
HuggingFace
Gemini
The Problem
- E-learning platforms received high volumes of repetitive student support queries that overwhelmed human staff.
- Students had no real-time tool to get precise answers from course materials outside business hours.
- Keyword-based search in LMS platforms failed to return contextually relevant answers to student questions.
- Manual support teams could not scale to match student query volume during exam periods and course launches.
- No automated system could provide consistent, accurate responses grounded in verified course content.
Gallery
Our Solution
- Built a RAG-powered Q&A engine using Google Gemini Pro and LangChain over a CSV-based e-learning knowledge base.
- Implemented FAISS vector store with HuggingFace embeddings for fast, semantic similarity search over course content.
- Developed a Streamlit chat interface delivering context-aware, real-time responses to student queries.
- Designed the ingestion pipeline to process structured course content, policies, and FAQs into searchable vectors.
- Ensured response accuracy by grounding every answer in retrieved knowledge base passages before generation.
Impact
80% fasterquery resolution
Reduced average query resolution time by 80% and eliminated redundant manual searches across departments.
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