Update evaluation logs

rag_eval_logs.jsonl

@@ -102,3 +102,4 @@
 {"timestamp": 1768468196.7116585, "query": "Based on the projects in the resume, what kind of startup would this candidate be best suited to found?", "retrieved_count": 4, "confidence": 0.9, "answer_known": true, "source_type": "mixed", "final_answer": "Based on the projects and technical expertise detailed in the resume, Vignesh LV is best suited to found a startup in the **Enterprise AI & Intelligent Document Processing (IDP)** space.\n\nSpecifically, his background suggests a high potential for a startup focused on **\"Automated Knowledge Synthesis for Legal or Corporate Research.\"**\n\nHere is why this specific startup type fits his profile:\n\n### 1. Core Product: RAG-based Knowledge Discovery\nVignesh has already built the foundational architecture for this. His project involving **Retrieval-Augmented Generation (RAG)** using **FAISS** and **ChromaDB** is the gold standard for startups trying to solve \"internal search\" problems. \n*   **Startup Application:** A platform that allows large organizations (law firms, medical researchers, or consulting groups) to upload thousands of proprietary documents and \"chat\" with their data to extract insights.\n\n### 2. Specialized Feature: Abstractive Summarization\nUnlike simple \"extractive\" tools that copy-paste sentences, Vignesh has worked with **T5 and BART** architectures for **Abstractive Summarization**. \n*   **Startup Application:** Most current AI tools struggle with \"hallucinations\" or lack of nuance. His experience with **ROUGE** and **BLEU** metrics suggests he understands how to fine-tune models for accuracy\u2014a critical requirement for professional-grade summarization tools that reduce reading time by 50% (as noted in his PDF Summarizer project).\n\n### 3. Business Value: Predictive Retention & Analytics\nHis **Telco Customer Churn Prediction** project shows he isn't just focused on \"cool\" AI, but on AI that impacts the bottom line. \n*   **Startup Application:** His startup could integrate **Predictive Analytics** into the document platform. For example, a tool that not only summarizes legal contracts but also uses ML to predict which contracts are \"at-risk\" of being breached based on historical data patterns.\n\n### 4. Technical Stack Readiness\nVignesh possesses the full-stack AI skills necessary for a technical founder:\n*   **Model Fine-Tuning:** Ability to adapt models like BERT or ResNet to specific industry niches.\n*   **Data Visualization:** Skills in **PowerBI** and **SQL** allow him to build the dashboards that corporate executives need to see the ROI of his AI solutions.\n*   **Rapid Prototyping:** His certifications from Oracle and IBM in Generative AI and Data Science show a commitment to staying current with the fast-moving AI landscape.\n\n### Final Startup Concept Recommendation: \"NexusDoc AI\"\n**The Pitch:** An AI-powered workspace for high-stakes industries (Legal/Finance) that uses RAG pipelines to manage internal knowledge, provides T5-based executive summaries of complex filings, and uses predictive modeling to identify operational risks within company documentation.", "context_list": ["**INTERNSHIP**\n\n\n**Machine Learning Intern** - Future Interns | Oct 2025 \u2013 Nov 2025\n\n\n  - Completed hands-on, ML-based task submissions, focusing on practical implementation, and\ndocumented workflows via GitHub and LinkedIn. - Gained exposure to real-world ML workflows, task reporting, and community-based learning\nwithin a professional environment. **EDUCATION & CERTIFICATIONS**\n\n\n**Education**\n\n\n  - **B.E. in Computer Science**   - SJMIT | Graduating 2025\n\n\n  - **Diploma in Computer Science**   - SJMP | 2022\n\n\n**Certifications**\n\n\n  - Oracle Generative AI Certificate\n\n\n  - Oracle AI Foundations\n\n\n  - IBM Python with Data Science\n\n\n  - IBM Python Programming with SQL", "Developed abstractive text summarization models utilizing advanced architectures ( **T5,**\n**BART** ) and rigorously evaluated performance using industry-standard metrics like **ROUGE**\nand **BLEU** . **Telco Customer Churn Prediction (ML)**\n\n\n  - Implemented and compared various ensemble ML models; selected the best-performing\nmodel based on crucial business metrics including **accuracy, F1-score, and ROC-AUC** . **PDF Summarizer (End-to-End NLP Tool)**\n\n\n  - Developed a practical NLP tool that significantly reduces manual reading time by\napproximately approximate 50% by generating concise text summaries.", "**VIGNESH LV**\n\n\n**7892939971** | **lvvignesh2122@gmail.com** | **LinkedIn:** vignesh-lv-a682b7 | **GitHub:**\nLVVignesh\n\n\n**PROFESSIONAL SUMMARY**\n\n\nAspiring **AI Engineer** with hands-on experience in Machine Learning, Deep Learning, NLP, and\n**Generative AI** . Proven ability to develop end-to-end solutions, including **LLMs, RAG pipelines, vector**\n**search systems (FAISS/ChromaDB),** and advanced model fine-tuning (BERT, T5, ResNet). Passionate\nabout research-driven development and deploying real-world intelligent systems.", "G pipelines, vector**\n**search systems (FAISS/ChromaDB),** and advanced model fine-tuning (BERT, T5, ResNet). Passionate\nabout research-driven development and deploying real-world intelligent systems. **TECHNICAL SKILLS**\n\n\n**Category** **Key Tools and Concepts**\n\n\n**Programming &**\nPython, PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, SQL\n**Frameworks**\n\n\n**LLMs, RAG, Vector Databases (FAISS, ChromaDB concepts)**, Generative AI,\n**Generative AI & NLP**\nDialogflow, BERT, T5, RoBERTa, Prophet\n\n\nDeep Learning, Computer Vision, Predictive Modeling, Sentiment Analysis,\n**Core AI & ML**\nCNNs, RNNs/LSTMs/GRUs\n\n\n**Tools & Databases** VS Code, Jupyter Notebook, Git, MySQL, PowerBI\n\n\n**PROJECTS**\n\n\n**RAG & Vector-based Document Search System**\n\n\n  - Engineered a **Retrieval-Augmented Generation (RAG)** pipeline using Python and **FAISS** for\nefficient semantic search and accurate document-based Question-Answering (Q&A)."]}
 {"timestamp": 1768468627.394085, "query": "\"How much are the fees for the Computer Science course?", "retrieved_count": 3, "confidence": 0.9, "answer_known": true, "source_type": "mixed", "final_answer": "Based on the records available in our database, I was unable to find specific fee information for the Computer Science course.\n\n**Data Insights:**\n*   **Search Parameters:** A targeted search was performed on the student records table for any entries matching the \"Computer Science\" course.\n*   **Outcome:** The query returned no results, suggesting that either the fee information for this specific course is not listed in this database or the course is not currently registered under that exact name.", "context_list": ["Query: SELECT fees FROM students WHERE course = 'Computer Science';\nResult: No results found.", "Query: SELECT fees FROM students WHERE course = 'Computer Science';\nResult: No results found.", "Query: SELECT fees FROM students WHERE course = 'Computer Science';\nResult: No results found."]}
 {"timestamp": 1768484407.3279426, "query": "\"Who is enrolled in the Master of AI course?\"", "retrieved_count": 1, "confidence": 0.9, "answer_known": true, "source_type": "mixed", "final_answer": "\u26a0\ufe0f **System Alert**: The AI service is currently experiencing high traffic (Quota Exceeded). Please try again in a few minutes.", "context_list": ["Query: SELECT name FROM students WHERE course = 'Master of AI';\nResult: [{'name': 'Vignesh Ladar'}, {'name': 'Rachel Green'}]"]}
+{"timestamp": 1768548847.881433, "query": "what is the abstarct ?", "retrieved_count": 4, "confidence": 0.9, "answer_known": true, "source_type": "mixed", "final_answer": "Based on the provided document excerpts, the abstract for this project (conducted at the **Dept. of CS&E, SJMIT, Chitradurga**) can be summarized as follows:\n\n### **Abstract Summary**\nThe project focuses on developing a system that converts **real-time voice data into text** to assist individuals who face challenges using traditional computer input methods. Specifically, it aims to help millions of people who are unable to use their hands for typing, as well as those who are deaf or hard of hearing.\n\n**Key components and features of the system include:**\n*   **Automatic Speech Recognition (ASR):** Captures live audio from lectures, meetings, or speeches and provides real-time transcription.\n*   **Natural Language Processing (NLP):** Enhances the transcribed text by understanding context, grammar, and punctuation. It also includes a **summarization** feature to condense lengthy recordings into concise notes.\n*   **User Interface (UI):** A dashboard for managing recordings and transcriptions, equipped with tools for editing, formatting, and annotating text.\n*   **Target Audience:** The system is designed for students and instructors for organized note-taking, legal professionals for court transcriptions, and individuals with disabilities to facilitate easier communication and study.\n\nThe system essentially captures voice input, processes it via specialized algorithms, and stores it in a \"text train\" that serves as a permanent, searchable record for the user.", "context_list": ["them, allowing them to communicate with people by using voice input\n\n\nto operate the computer. Think about the millions of persons who are unable to use their\n\n\nhands, making codification impossible. The operation captures real time voice data and\n\n\nprocesses it by using algorithm and converts it into text and simultaneously it will get stored\n\n\nin text train which can be appertained as a note for instructors as well the people who have\n\n\ndifficulty to use their hands or deaf. Dept. of CS&E, SJMIT, Chitradurga Page 6", "ding more organized notes. **2.** **Automatic Speech Recognition (ASR):**\n\n\n   - **Real-Time Transcription** : This feature promptly transforms live audio from\n\nlectures or meetings into written text. Batch transcriptions process previously\n\nrecorded audio files to generate text notes. **3.** **Natural language processing, or NLP:**\n\n\n   - **Contextual Understanding** : Interprets grammar, punctuation, and context to\n\nproduce writing that makes sense. - **Summarization:** Highlights key ideas and condenses lengthy recordings into\n\nconcise notes. **4.** **User interface, or UI:**\n\n\n   - **Dashboard:** a consolidated recording and transcribing management interface. The\n\nediting tools enable users to format, edit, and annotate the transcribed text.", "es, and speeches, and utilize the system to arrange and refine their content. - **Lawyers and Legal Professionals:** Demand exact transcription of court\n\nproceedings, depositions, and client meetings. Employ the system to transcribe legal\n\nproceedings, organize case files, and guarantee the accuracy of legal documents. - **Individuals with Disabilities:** Require user-friendly tools for learning and\n\ncommunication, and need clear and concise text representations of audio content. Utilize the system to convert audio into text, facilitating easier studying, working,\n\nand communication.", "ure that all components function together properly\n\nand that integrations are effective. - **End User Testing (UAT):** Perform testing with actual users to collect insights\n\nand pinpoint any problems. **4.Deployment:**\n\n\n   - **Setup Production Environment:** Configure the production environment,\n\nincluding servers, databases, and security protocols. - **Data Transition:** Transition any relevant data to the new system. - **System Launch** : Go live with the system and make it accessible to users. **5.** **Maintenance and Support:**\n\n\n   - **System Monitoring:** Continuously track system performance and user feedback. - **Enhancements and Updates:** Regularly update the system to resolve issues,\n\nenhance performance, and introduce new features based on user suggestions. Dept."]}