"Validating your startup idea shouldn't feel like guessing. What if you had an AI cofounder that’s read everything Paul Graham, Y Combinator, and OpenAI ever wrote?"
Welcome to HustleGPT — a Generative AI-powered assistant that helps early-stage founders validate ideas, plan MVPs, and make smarter decisions, faster. This blog walks you through how I built the backend brain of HustleGPT using Google’s Gemini Pro and LangChain, as part of the Google x Kaggle GenAI Capstone.
🧠 Problem: Startup Guidance is Often Confusing
Early-stage founders often struggle to get actionable feedback on:
- Is my idea viable?
- What’s the MVP?
- Who are my competitors?
- Will this idea make money?
These are critical questions — but answers are scattered across dozens of blogs, videos, and forums. That’s where Generative AI can step in.
💡 Solution: HustleGPT
HustleGPT is a prototype GenAI agent that:
✅ Takes in a startup idea
✅ Grounds it in trusted startup literature
✅ Responds like a cofounder, mentor, or VC — powered by Gemini Pro
🔧 What We Built
This project focused on the core backend logic, using:
- 🧠 Gemini Pro (Chat + Embeddings) via
langchain-google-genai - 📚 RAG (Retrieval-Augmented Generation) grounded in:
- Paul Graham’s essays
- Y Combinator’s startup guide
- OpenAI’s startup advice
- 🔍 Dynamic Query Interface — ask startup-related questions and get grounded, AI-driven responses.
🔍 Generative AI Capabilities Used
| Capability | Description |
|---|---|
| 🧠 Gemini Chat | Powers natural Q&A and startup strategy prompts |
| 🔎 Gemini Embeddings | Converts documents into searchable vectors |
| 📚 RAG Pipeline | Combines search + generation for grounded answers |
🔗 Sources Used for Grounding
We used publicly available, high-quality startup literature:
- https://openai.com/blog/startup-advice
- https://www.ycombinator.com/library/6h-how-to-start-a-startup
- https://paulgraham.com/start.html
- https://www.kaggle.com/competitions/google-generative-ai-capstone-q1-2025/overview
- https://cloud.google.com/vertex-ai/docs/generative-ai/overview
- https://python.langchain.com/docs/get_started/introduction
These documents were chunked, embedded, and indexed via LangChain’s VectorStore, enabling real-time semantic search during inference.
🛠️ Tech Stack
-
langchain-google-genaifor Gemini API - Gemini Pro (text + embeddings)
- LangChain RetrievalQA
- Python
- Jupyter Notebook
🎯 Sample Queries
Here are some grounded prompts that worked well:
# Example 1
query = "What advice does Y Combinator give to startup founders?"
# Example 2
query = "How does Google Vertex AI support generative AI?"
# Example 3
query = "What are common mistakes startup founders make according to Y Combinator?"
📝 Note: Due to limited document sources, not all queries produce perfect answers yet. This is expected and can be improved with more diverse URLs or a custom dataset.
📌 Architecture Overview
+--------------------------+
| User Query |
+--------------------------+
|
v
+--------------------------+
| Gemini Embeddings | ← Document Vectorization
+--------------------------+
|
v
+--------------------------+
| LangChain VectorStore | ← Semantic Search
+--------------------------+
|
v
+--------------------------+
| Gemini Pro (Chat Model) | ← Grounded Answer Generation
+--------------------------+
🤖 How it Works (Simplified):
Load URLs → Clean Text → Chunk
Embed Chunks via Gemini Embeddings
Store vectors in LangChain VectorStore
User Query → Similar Chunks → Gemini Pro → Answer
🧪 What’s Working
✅ Fully working RAG pipeline
✅ Modular backend ready for frontend integration
✅ Real-time querying against startup documents
✅ Gemini integration with LangChain
🔮 What’s Next
This notebook lays the foundation for a full-stack app. The next phase includes:
- 🌐 Frontend (React/Streamlit)
- ⚙️ Backend API (FastAPI)
- 📝 GitHub (Code Repository)
📌 Learnings
- RAG is powerful, but depends heavily on document quality.
- Gemini Pro + LangChain works beautifully with the right setup.
- It's important to guide the user with good prompt examples.
- Timeboxing and narrowing scope is key to shipping.
✨ Closing Thoughts
Building HustleGPT taught me how Generative AI can amplify early-stage innovation — not by replacing founders, but by giving them sharper tools.
If you're a student, hacker, or builder curious about startups, I hope this inspires you to build your own AI-powered idea validator. The journey is worth it 🚀
💬 Tried HustleGPT? Let me know what startup ideas you tested or what you'd like to see in V2.
📬 Connect with me on LinkedIn or drop a comment on the notebook!
🔗 Useful Links
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