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About the Project

Inspiration

The inspiration came from a common problem: searching through large documents is time-consuming and frustrating. I wanted to build something that could instantly understand and retrieve the right information, instead of making people manually scan through pages. That’s where Retrieval-Augmented Generation (RAG) felt like the perfect solution.

What I Learned

• How RAG pipelines combine vector databases and LLMs to deliver context-aware answers.
• The importance of prompt engineering for better accuracy.
• Handling unstructured data and converting it into embeddings for fast retrieval.
• Deployment strategies to make the system scalable and user-friendly.

How I Built It

• Preprocessed documents into chunks and generated embeddings.
• Stored embeddings in a vector database for quick retrieval.
• Integrated an LLM with the retrieval pipeline to provide context-based answers.
• Built a simple UI where users can upload documents and ask natural language questions.

Challenges

• Ensuring accurate answers without hallucinations.
• Optimizing embedding size and retrieval speed.
• Balancing between cost and performance while using APIs.
• Creating a user-friendly interface that feels intuitive.

Built With

• ai
• mongodb
• node.js
• rag
• react
• vector