📚 ResearchBuddy – AI-Powered Research Query Processing System

🔗 Demo Video

▶ YouTube: https://youtu.be/fjoqoht0wjE

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🚀 Overview

ResearchBuddy is an AI-augmented, research-oriented query processing system designed to help students, early-stage researchers, and innovators quickly generate research directions, topics, literature summaries, and structured research plans.

The system interprets user queries using a lightweight intent-classification layer and routes them through predefined research prompt-pipelines. Each pipeline maintains a controlled, multi-step generation workflow that ensures accuracy, domain relevance, and research-grade structuring.

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🎯 Key Features

✅ 1. Lightweight Intent Classification

• Detects whether the query is about:
  • Research topics
  • Problem statements
  • Literature surveys
  • Methodology design
  • Research plan & timeline
• Enables fast, domain-aware routing.

✅ 2. Modular Prompt-Pipeline Architecture

Each pipeline follows structured stages such as:

• Problem understanding
• Domain analysis
• Literature summary
• Research gaps
• Solution direction
• Final compiled output

This ensures reliability and controlled generation.

✅ 3. End-to-End Research Plan Generation

Automatically creates:

• Clear problem definition
• Proposed approach
• Evaluation metrics
• Tools/tech stack recommendations
• Future enhancements

✅ 4. Domain-Aware Output

The model adapts responses based on:

• CS/AI
• Data Science
• Engineering
• Social sciences
• Business & management
• Any research domain entered by the user

✅ 5. Student-Friendly

Perfect for:

• Assignments
• Semester projects
• FYP ideation
• Hackathon submissions
• Research proposals

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🧠 How It Works (Technical Flow)

1. User Query → Intent Detection

   • Uses a lightweight classifier to map input to a research task type.

2. Pipeline Routing

   • The system selects a predefined structured prompt-pipeline (topic generator, literature analyzer, research planner, etc.)

3. Multi-Stage Output Generation

   • Each pipeline consists of 5–9 controlled stages.
   • Ensures factual, logically coherent, and domain-specific results.

4. Response Formatting Layer

   • Outputs standardized, clean, research-ready content.

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🧩 System Architecture

flowchart TD
    A[User Query] --> B[Intention Classifier]
    B --> C{Select Pipeline}
    C --> D[Topic Generator Pipeline]
    C --> E[Literature Analysis Pipeline]
    C --> F[Research Plan Pipeline]
    D --> G[Structured Output]
    E --> G
    F --> G

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📝 Example Queries

• “Give me AI-based research topics in healthcare.”
• “Summaries of recent papers on federated learning.”
• “Create a research plan for predicting crop yield using ML.”
• “Explain research challenges in robotics perception.”

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📦 Outputs You Can Generate

• 🔹 Research topics
• 🔹 Problem statements
• 🔹 Challenge analysis
• 🔹 Literature summaries
• 🔹 Methodology workflows
• 🔹 End-to-end research plans

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🏆 Hackathon Context

This project was developed for a hackathon challenge focused on AI-powered research assistance, solving the problem of:

“Students struggle to explore, navigate, and structure research information efficiently.”

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📜 License

MIT License

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⭐ Support

If this project helped you, don't forget to star ⭐ the repository!

This project was created using Google AI Studio (Gemini 3 Pro – DeepMind Vibe Code).