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| # jntugv-hackathon-dec-2025 | |
| Hackathon for JNTU Vijayanagaram - December 13, 2025 | |
| ### Guidelines for Participants: | |
| 1. You initiate the hackathon by forking the Git repository given by GenAIVersity, which | |
| contains instructions and guidelines for the event. | |
| 2. Your initial change must involve updating the README.md file to include a | |
| comprehensive problem statement and solution description. | |
| 3. You are required to exhibit the implementation of solutions in the domains of | |
| Generative AI (ChatBots, RAG, Agents, and Agentic AI) and Machine Learning | |
| technologies utilizing Python, JavaScript, or TypeScript. | |
| 4. You must possess foundational knowledge of Machine Learning principles, LLM | |
| parameters, embeddings, prompt engineering, context engineering, RAG, agents, | |
| agentic AI, MCP, LangChain, ChromaDB, and token generation. | |
| 5. It is beneficial to possess understanding regarding Guardrails and Evaluations which | |
| has additional weightage for evaluation. | |
| 6. If you are utilizing local LLMS, ensure they are downloaded and prepared prior to the | |
| hackathon, since they require significant internet bandwidth. If employing external APIs | |
| such as OpenAI, Gemini, or Anthropic, you must provide your API key, | |
| as GenAIVersity does not supply one. | |
| 7. You are permitted to utilize Generative AI tools such as ChatGPT, Gemini, Perplexity, | |
| and coding tools like Copilot and Cursor; however, you must retain conversation history | |
| and safeguard it from deletion. | |
| 8. Select a problem statement from any domain, preferably: a) Education b) Finance c) | |
| Healthcare d) Telecom e) Productivity f) Technological Innovation. Sample problems are | |
| included in the attached document for your comprehension; you may select and adapt | |
| the ideas presented. | |
| 9. It's nice to have the user interface(UI/Frontend), but it doesn't help with review much | |
| because managing time during a hackathon is very important. | |
| ### Assessment Standards | |
| 1. 25% Innovation | |
| 2. 25% Technical Implementation | |
| 3. 25% Utilization of Artificial Intelligence | |
| 4. 15% Impact and Expandability | |
| 5. 10% Presentation | |
| ### Submission Checklist | |
| - Updated README.md (problem, data link, design, assumptions). | |
| - Reproducible Notebook(s) and/or minimal FastAPI service (no UI required). | |
| - requirements.txt / environment.yml and run commands. | |
| - Evaluation notes (metrics, tests, guardrails, limitations). | |
| - Commit history & AI chat logs (attach/export or link). | |
| ## VERY IMP NOTE: A 10min demonstration video has to be screen recorded with your voice and should be shared through YouTube link. | |