The RAG Evaluation Framework is a generative AI project that combines Chunking strategy and Retrieval layer, built with LangChain / LlamaIndex. 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 Retrieval layer and LangChain / LlamaIndex, 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 Chunking strategy, orchestrated with LangChain / LlamaIndex and Retrieval layer. 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.
LangChain / LlamaIndex
Chroma / FAISS / Pinecone
Python 3.11+
Structured prompts and configuration
Error handling and retries
Evaluation and logging
OpenAI embeddings / sentence-transformers
Modular pipeline around Chunking strategy and Retrieval layer
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 Chunking strategy; the generation layer invokes the model with LangChain / LlamaIndex and Retrieval layer; and the output layer formats and presents results via Citation and evaluation module. 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 Chunking strategy and validate incoming data.
Implement the generation pipeline using LangChain / LlamaIndex and Retrieval layer.
Add the output and presentation layer via Citation and evaluation module.
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 Grounding answers with sources and RAG evaluation and tuning
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 Evaluation Framework 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.
