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Stories Worth Sharing:5-Day Gen AI Intensive Course with Google

For our Kaggle Capstone, we challenged ourselves to build something real: a chatbot that could answer FAQs for our own company using GenAI tools. The goal? Create a working prototype, learn as much as possible, and explore how powerful and accessible today’s AI really is.

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Stories Worth Sharing:

Why This Project?

Every business deals with repetitive questions. A static FAQ page helps, but it’s not always user-friendly. We wanted something better — a conversational assistant that could understand and respond naturally.

The Tech Behind the Bot

Our project aimed to create exactly that. Here’s a breakdown of what the system does:

  • Knowledge Base: A list of 40 company-specific Q&A pairs.
  • Embeddings: We used Google’s embedding-001 model to convert these into vector representations — the backbone for finding meaning in text.
  • User Input: A chat interface (built with ipywidgets) lets users ask questions.
  • Semantic Search: User queries are embedded and compared to the FAQ list using cosine similarity to find the closest match.
  • RAG (Retrieval-Augmented Generation): If a match is strong enough, Gemini 1.5 Flash generates a natural-sounding response based on the relevant FAQ.
  • Fallback: No good match? The bot politely admits it doesn’t have the info and suggests speaking to a human.

All of this runs inside a Kaggle Notebook, using the google.generativeai library, with help from ChatGPT for brainstorming and code cleanup.

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The Code (A Glimpse)

The entire project lives within a Kaggle Notebook.

The Python code uses the google.generativeai library to interact with the Gemini API for both embedding creation and answer generation. Key parts include:

  • A cosine_similarity function (using numpy) to compare embeddings.
  • A core find_best_match function to perform the vector search.
  • Separate LLM prompting functions: one for generating answers from context (RAG) and another for the "low confidence" fallback response.
  • The main smart_faq_bot_gemini function orchestrating the process: embedding the query, finding the match, deciding whether to use RAG or the fallback based on the similarity score.
  • A simple interactive UI using ipywidgets for demonstration within the notebook. But that is rather for the Demo purpose and has improvement potential.

Honest Reflections: Successes and Shortcomings

Now, for some real talk:

Can the code be improved? Absolutely. It’s functional but could be more robust, modular, and optimized. Error handling is present but could be more sophisticated. Using a proper vector store would be better than a simple list for larger datasets.

Is the model usage economically wise? Probably not in its current state. Each query involves embedding calls and a generation call. Optimization (e.g., caching, potentially cheaper models, prompt tuning) would be crucial for real-world deployment.

Does it solve the real-world problem? It demonstrates a proof-of-concept and works for the defined 40 FAQs. However, for Peak Pioneers’ actual use, it would need significant refinement, more comprehensive data, better evaluation, and integration capabilities. It’s a starting point, not a finished product.

Does this project show GenAI empowers ordinary people?

YES. 100% YES.

This is the most crucial takeaway for me. Without AI assistance (especially Gemini for coding and ChatGPT for ideas), I, as someone who isn't a deep AI/ML developer, could not easily have built something like this. It democratizes development in a way I haven't experienced before.

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Open Questions?

Let us know if you desire any additional info about the project or the concept. We are happy to share more details.

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