Built My Own AI Study Partner Using RAG — Now I Understand Research Papers 3x Faster! 📚🤖
𝗛𝗲𝗿𝗲’𝘀 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗜 𝘁𝗿𝗶𝗲𝗱 𝘁𝗵𝗶𝘀 𝘄𝗲𝗲𝗸 𝘄𝗶𝘁𝗵 𝗔𝗜! 🚀 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗮 𝗽𝗮𝗿𝘁𝗻𝗲𝗿 𝗳𝗼𝗿 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝗱 𝘀𝘁𝘂𝗱𝗶𝗲𝘀!
We know AI has incredible capabilities, but I wanted to use these capabilities to enhance my own knowledge, rather than relying on AI every time I need an answer — without engaging my own thinking.
With the fast-growing knowledge base in AI and related technologies, as a technology enthusiast, I wanted to keep up with ongoing research and trends despite the limited time I have alongside work and family responsibilities.
With this idea in mind, I built something interesting using the RAG (Retrieval-Augmented Generation) framework!
🔵 𝗙𝗼𝗿 𝘁𝗵𝗼𝘀𝗲 𝗻𝗲𝘄 𝘁𝗼 𝗥𝗔𝗚:
It enhances the capabilities of Large Language Models (LLMs) by integrating information retrieval — accessing relevant data from a knowledge base — with the text generation capabilities of LLMs.
In simple terms, it provides additional, specific information to a generalized LLM to answer questions related to my personal documents.
This capability is now heavily used by enterprises, where they combine generalized LLMs with their internal data to create employee-specific AI tools.
Using this idea, I built my homegrown LLM system:
- I created a vector database using FAISS (Facebook AI Similarity Search).
- I uploaded research papers and documents related to my interests (AI, ML, etc.).
- I generated embeddings from these documents.
- My system retrieves relevant content and feeds it to the LLM to answer my questions!
✅ For example, when I ask:
“𝘚𝘶𝘮𝘮𝘢𝘳𝘪𝘻𝘦 𝘵𝘩𝘪𝘴 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩 𝘱𝘢𝘱𝘦𝘳”,
𝘵𝘩𝘦 𝘴𝘺𝘴𝘵𝘦𝘮 𝘧𝘦𝘵𝘤𝘩𝘦𝘴 𝘵𝘩𝘦 𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘵 𝘱𝘢𝘳𝘵𝘴 𝘢𝘯𝘥 𝘵𝘩𝘦 𝘓𝘓𝘔 𝘱𝘳𝘰𝘷𝘪𝘥𝘦𝘴 𝘢 𝘤𝘳𝘪𝘴𝘱 𝘴𝘶𝘮𝘮𝘢𝘳𝘺 — 𝘧𝘢𝘴𝘵𝘦𝘳 𝘢𝘯𝘥 𝘮𝘰𝘳𝘦 𝘢𝘤𝘤𝘶𝘳𝘢𝘵𝘦!
Before this, it used to take me 3–4 hours to thoroughly understand a research paper.
Now, it takes just around 1 hour!
This gives me additional time to dive deeper into topics, extract more insights, and even write articles based on my learning.
Thank you, AI Buddy, for helping me in combined studies! 🤝
👉 GitHub Repository — https://lnkd.in/gxmEW2G9 Feel free to explore!
