The RAG with Source Citations is a generative AI project that combines Citation and evaluation module and Embedding generation, built with Pandas. The project follows a clean, modular pipeline where input handling, generation, and presentation stay separated, making it easy to test, extend, and present. It showcases modern generative AI techniques while producing a working, demo-ready application.
Traditional solutions to this problem are slow, static, and unable to generate new, context-aware content on demand. Without a generative AI approach built on Embedding generation and Pandas, users cannot get personalized, high-quality outputs quickly, and there is no straightforward way to refine or evaluate the results.
This project applies generative AI through Citation and evaluation module, orchestrated with Pandas and Embedding generation. The pipeline is designed for quality and control, with validation, evaluation, and a clean interface. It generates consistent, context-aware results and can be adapted to related tasks with minimal changes.
Pandas
LangChain / LlamaIndex
Python 3.11+
Structured prompts and configuration
Error handling and retries
Evaluation and logging
OpenAI embeddings / sentence-transformers
Modular pipeline around Citation and evaluation module and Embedding generation
Configurable generation and evaluation settings
Clear logging, retries, and cost tracking
Clean interface for results
Reusable components for related tasks
Evaluation of output quality
The project is layered: the input layer prepares and validates inputs through Citation and evaluation module; the generation layer invokes the model with Pandas and Embedding generation; and the output layer formats and presents results via Retrieval layer. Shared configuration, logging, and evaluation modules support all layers, keeping the system robust and easy to extend.
Set up the Python environment, project structure, and configuration files.
Build the input layer with Citation and evaluation module and validate incoming data.
Implement the generation pipeline using Pandas and Embedding generation.
Add the output and presentation layer via Retrieval layer.
Wire up end-to-end flows and add error handling and retries.
Evaluate output quality, tune prompts, and refine settings.
Package the project, document it, and prepare the demo and viva report.
Build production-style generative AI applications
Apply Document chunking and embedding and Vector databases and retrieval
Design prompts and evaluation for generated content
Work with LLM and diffusion model APIs
Present and defend a complete GenAI project in viva
Expose the pipeline as a REST API for other apps
Add fine-tuning for higher-quality domain outputs
Add guardrails and content safety checks
Deploy with caching for lower latency and cost
The RAG with Source Citations delivers a complete generative AI workflow — from input and generation to evaluation and presentation. It is practical, modern, and easy to explain, making it an excellent final year project that demonstrates in-demand AI skills.
