📝 Project Publication: Retrieval-Augmented Generation (RAG) Assistant
📌 Abstract
This project is part of the Agentic AI Developer Certification (AAIDC) Module 1 and demonstrates the development of a Retrieval-Augmented Generation (RAG) Assistant. The assistant enhances AI reliability by combining document retrieval with natural language generation, producing accurate and context-aware responses.
🔍 Introduction
Large Language Models (LLMs) are powerful but often prone to hallucinations, generating incorrect or unverifiable answers. This project addresses that challenge by implementing a RAG architecture, grounding responses in actual documents. The goal is to showcase how retrieval mechanisms can be integrated with LLMs to build fact-based, trustworthy assistants.
⚙️ System Overview
Workflow
Document Loading: Text documents (.txt) are ingested from a designated folder.
Chunking: Documents are split into smaller chunks to fit within token limits and improve retrieval granularity.
Embedding Generation: Each chunk is converted into a semantic vector using OpenAI Embeddings.
Vector Database Storage: Embeddings are stored in Chroma, enabling efficient similarity search.
Retrieval: On receiving a query, the system retrieves the most relevant chunks.
Response Generation: Retrieved context is passed to an LLM (e.g., GPT-3.5) via LangChain, which generates an accurate and natural-sounding answer.
Tools & Frameworks
LangChain: Workflow orchestration
OpenAI Embeddings: Semantic vectorization
Chroma: Vector database for retrieval
Streamlit: Interactive user interface
✨ Key Features
Context-Aware Responses: Answers are grounded in retrieved documents, reducing hallucinations.
Modularity: Components (LLM, embeddings, vector database) can be easily swapped.
Scalability: New documents can be added, and embeddings updated dynamically.
User-Friendly Interface: Streamlit frontend for interactive querying.
📚 Example Use Case
Scenario: A knowledge base of company policies is loaded into the system.
User Query: “What is the company’s leave policy for interns?”
RAG Assistant Response: Retrieves and cites the specific policy from documents, providing a concise, reliable answer.
This illustrates how the assistant can support education, enterprise knowledge management, and research assistance.
🧪 Evaluation & Best Practices
Chunking ensures efficient token usage.
Semantic similarity search retrieves relevant information even when queries are phrased differently.
Grounded answers reduce hallucinations common in pure LLM setups.
Modular pipeline enables easy integration with new tools or APIs.
⚠️ Limitations
Dependent on the quality and coverage of the uploaded documents.
Performance may vary with very large datasets or highly ambiguous queries.
Currently optimized for text files; multi-format support (PDF, CSV) can be added in future iterations.
✅ Conclusion
This project demonstrates a complete RAG pipeline from ingestion to intelligent question answering. By grounding LLM responses in retrieved documents, it balances accuracy, reliability, and usability, offering both educational value and a foundation for real-world AI assistants.