Genuis - Genuinely making you a Genius

Inspiration

The last thing you want your tutor to do is lie to you. Yet, 77% of users have been deceived by AI hallucinations when using chatbots for learning (Tidio). Students deserve to study with confidence, accuracy, and trust, but traditional AI tutors often fabricate information, leading to confusion and misinformation.

We built Genuis because students shouldn’t have to second-guess and cross-reference their study tools. Unlike general AI models, which prioritize versatility over accuracy, Genuis only provides answers grounded in real course materials and lectures. Whether you're reviewing for an exam or tackling a tough assignment, Genuis ensures that your AI tutor is as reliable as your professor.

What it does

Genuis is an AI-powered study assistant that delivers fact-checked, course-specific tutoring by pulling directly from uploaded lecture notes, textbooks, and classroom materials. It never hallucinates answers—it simply doesn't generate responses unless it finds relevant context in your uploaded resources.

And for those tired of copying and pasting to feed a chat bot content you want clarification on—we do exactly that! We keep an extensive database of all the prompts you’ve entered previously which is often refactored and holds on to the contents of pdfs, html, and even youtube videos that can deliver your answers and thorough understanding. Ensuring your minimal input, the database considers specific courses, professors, office hours, assignments, lectures, and much more so that you get answers at the click of a button. And the icing on the cake—our voice feature with vocal output option that makes it so you don’t even have to read!

Challenges We're Proud we Handled

1. Efficient Data Retrieval with Elastic Database One of our biggest challenges was efficiently keeping track of past user prompts and their associated resources. To solve this, we leveraged an Elastic database that stored:Past user prompts, the most relevant link associated with each prompt, and the type of link (PDF, HTML, YouTube, etc.) To optimize retrieval, we vectorized both user prompts and the content of stored links. When a new query came in, we used cosine similarity (a vector comparison method) and vector regression analysis to compare the new query against existing prompt vectors and content vectors in our database. If we had enough relevant information, we could provide an answer without redundant scraping, significantly improving efficiency.

2. Deciding When to Search vs. Answer Directly A major design challenge was determining whether to: Let the LLM answer a question based on its existing knowledge. Search our database for relevant information. Scrape new data from the web. To solve this, we had the LLM rate its confidence (on a scale from 1 to 10) regarding its ability to answer a given question without additional context. If the confidence score was below 8, we either searched our database or scraped new data. This approach minimized unnecessary API calls and improved overall runtime efficiency.

3. Finding the Most Relevant Link When multiple potential sources were available, we needed a way to determine the best link for answering a user's question. We solved this by leveraging Perplexity’s Sonar API, which intelligently ranked links based on relevance. This ensured that users received the most accurate and high-quality information available.

Advantages You Can Get Behind

🔍 Smarter Than Your Browser Tabs No more endless Googling, clicking on sketchy forum posts, or drowning in 50 open tabs. Our AI-powered search actually understands your question and fetches the most relevant answers—instantly.

⚡ Speed Demon – Thanks to our universal vector database magic and cosine similarity wizardry, we don’t waste time scraping the web if we already have what you need. That means less waiting, more learning.

🗣️ Talks Back to You! Our dynamic text-to-speech feature lets your device read out responses, making interactions more fun and accessible!

🧠 Memory of an Elephant – Unlike search engines that forget you exist after each query, our assistant remembers past interactions. It can recall what you’ve asked before, suggest better resources, and even track your academic progress over time.

🤖 AI That Knows When to Chill – Ever seen an AI panic-search for answers it already knows? Not here. Our LLM is self-aware enough to rate its confidence and only fetch external data when necessary.

📚 PDFs, YouTube, and More—Oh My! – Whether it’s lecture slides, online textbooks, or video tutorials, we go beyond basic web pages. Even that ancient PDF your professor uploaded in 2013 isn’t safe from our AI’s retrieval powers.

📅 Your Study Buddy (Who Actually Cares) – We’re not just here to answer questions—we organize your learning. Need to review circuits before your exam? Our system can suggest study sessions, set up meetings with advisors, and even integrate with your calendar. AI-powered academic life-hacks, anyone?

💡 Not Just Smarter—Getting Smarter – The more you use it, the better it gets at knowing what you need. With ongoing ranking improvements and real-time feedback, this isn’t just a tool—it’s an evolving study companion, who genuinely wants to see you thrive.

What we learned

Optimizing Efficiency A key challenge was balancing response speed and accuracy. To optimize this, we implemented a confidence-based retrieval system, where the LLM rated its ability to answer a query without additional context. If the confidence was below 8, the system either queried our vector database or performed a web search, reducing unnecessary API calls and improving runtime efficiency.

To prevent redundant data retrieval, we leveraged Elasticsearch to store past prompts, relevant links, and vectorized content. Using cosine similarity, we compared new user queries with stored data to determine whether an existing answer was sufficient before scraping new information. This approach significantly reduced latency and minimized redundant external requests.

Tools & Design Choices We designed our system using FlutterFlow for the front end and Python APIs for backend processing. Key tools and integrations included:

OpenAI API for natural language processing and query understanding Perplexity Sonar API for selecting the most relevant sources Text-to-Speech APIs to enhance accessibility Mathematical methods (vectorization, cosine similarity) for ranking query relevance Elasticsearch for efficient data retrieval and indexing These choices allowed us to create a system that prioritizes efficiency while maintaining high accuracy. By combining structured data storage, intelligent retrieval methods, and API-driven search capabilities, we ensured a fast, relevant, and user-friendly experience.

What's next for Genuis - Your Genuine Genius

One of our main goals moving forward is to expand how we use the vector database to provide a more personalized and proactive academic assistant. Currently, we store past user prompts and relevant resources, but we want to take this further by organizing entire conversations to track student learning progress over time.

By analyzing a student’s search history and previous interactions, our system could:

Periodically update conditionals based on their coursework and study patterns. Suggest study sessions when it detects gaps in understanding. Recommend meeting with an advisor if it notices frequent queries related to struggling topics or career planning. Integrate with calendars to automatically set up study sessions, tutoring, or academic advising meetings based on workload and deadlines.