| Project Overview | This project direction uses recent LLM-for-software-engineering research to improve software development tasks. The implementation can support code review, bug triage, testing, requirement extraction, documentation generation, or developer knowledge retrieval. The reference paper, 'Retrieval-Augmented Generation for Large Language Models: A Survey', provides the academic base for the topic. Instead of copying the paper abstract directly, this page keeps the same research intent in a safe paraphrased form: the system retrieves relevant document chunks before generating answers, improving factual grounding and reducing unsupported responses. The final student implementation can include dataset preparation, model/API integration, dashboard or app interface, result explanation, and a short documentation-ready workflow. |
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| Research Paper Title | Retrieval-Augmented Generation for Large Language Models: A Survey |
| Research Paper / PDF Link | Open Paper / PDF |
| Year | 2023 |
| Project Area | Software Engineering with AI |
| Project Type | Software Project |
| Required Tools / Software | Python, GitHub API, LLM API, Streamlit/React, Static analysis tools, Vector DB |
| Main Features / Working Principle | Collect or upload relevant data, preprocess it, apply an AI/ML/LLM/RAG/software workflow, and present the result through a dashboard or application interface for Software Engineering with AI. |
| Expected Output | A working B.Tech project prototype for Software Engineering with AI with input, processing, result display, and explanation/report sections. |
| Possible Add-ons | Admin panel, PDF report export, model comparison, source citations, login system, WhatsApp help button, and deployment on cloud/hosting. |
| Get Help | Get Help on WhatsApp
Message: Hi FE, I need help with "RAG-Based Internal Developer Knowledge Assistant" in "Computer Science & Engineering" |
This B.Tech Computer Science & Engineering project resource connects a recent research direction with a practical implementation plan, tools, expected output, and possible extensions.