Introduction

Large Language Models (LLMs) are capable of generating fluent and informative responses, but they often suffer from hallucinations when answering questions that require factual accuracy or domain-specific knowledge beyond their training data. This limitation poses significant challenges for real-world applications that rely on trustworthy, document-grounded information.

Retrieval-Augmented Generation (RAG) addresses this issue by combining information retrieval techniques with generative language models. Instead of relying solely on parametric knowledge, a RAG system retrieves relevant document segments from an external knowledge source and uses them as context for response generation. This approach helps ensure that generated answers are grounded in actual data, improving reliability and interpretability.

The purpose of this project is to demonstrate the design and implementation of a domain-specific question answering system using a Retrieval-Augmented Generation pipeline. The primary objective is to reduce hallucinations, improve answer accuracy, and maintain contextual continuity across long documents through effective chunking, embedding-based retrieval, and controlled prompt engineering.

This publication presents a practical RAG implementation that includes document ingestion, text chunking with overlap, vector embeddings, similarity-based retrieval, and response generation using a large language model. The system is intentionally scoped to operate on a defined document domain, making it suitable for applications such as technical documentation assistants, knowledge bases, and enterprise question answering systems.

Problem Statement

While general-purpose language models can answer a wide range of questions, they lack access to up-to-date or domain-specific information and may generate incorrect or fabricated responses when queried about specialized content. This becomes a critical issue in scenarios where accuracy, traceability, and trustworthiness are required, such as technical documentation analysis or knowledge-based systems.

The challenge addressed in this project is to design a system that enables a language model to answer questions based strictly on a given document corpus, ensuring that responses are grounded in retrieved context rather than unsupported assumptions. The system must also handle long documents effectively while preserving semantic continuity across sections.

System Architecture

The system follows a standard Retrieval-Augmented Generation architecture composed of two primary stages: retrieval and generation.

1. Document Ingestion
   Raw documents are loaded and preprocessed to prepare them for indexing.

2. Chunking and Embedding
   Documents are split into smaller overlapping chunks to preserve contextual flow. Each chunk is converted into a dense vector representation using an embedding model.

3. Vector Store
   The embeddings are stored in a vector database that enables efficient similarity search.

4. Query Processing and Retrieval
   User queries are embedded and compared against stored document vectors to retrieve the most relevant chunks.

5. Response Generation
   Retrieved chunks are passed as context to a large language model, which generates a final answer grounded in the provided documents.

RAG Pipeline Workflow

The Retrieval-Augmented Generation pipeline operates as follows:

1. A user submits a natural language query.
2. The query is embedded into a vector representation.
3. The vector database performs similarity search to identify the top-k most relevant document chunks.
4. Retrieved chunks are combined into a contextual prompt.
5. The language model generates a response using only the retrieved context.

By separating retrieval from generation, the system ensures that responses remain grounded in the source documents, reducing hallucinations and improving factual accuracy.

Chunking and Overlap Strategy

To effectively process long documents and preserve semantic continuity, the system employs a chunking strategy with controlled overlap. Documents are divided into fixed-size text chunks, each overlapping with adjacent chunks by a predefined number of tokens. This overlap ensures that important contextual information spanning chunk boundaries is not lost during retrieval.

Chunk overlap is particularly important when answering questions that reference information split across multiple sections of a document. Without overlap, relevant context may be fragmented, leading to incomplete or less accurate responses. The chosen chunk size and overlap represent a balance between contextual coverage and computational efficiency, as larger overlaps increase retrieval quality but also raise storage and processing costs.

Project Scope and RAG Configuration

The scope of this project is intentionally limited to a specific document domain to ensure focused and meaningful retrieval. By constraining the knowledge base to a defined set of documents, the system avoids irrelevant results and improves answer precision.

Key configuration parameters of the RAG pipeline include:

• Chunk size: Controls the amount of text in each document segment
• Chunk overlap: Maintains continuity between adjacent chunks
• Embedding model: Converts text into dense vector representations
• Top-k retrieval: Determines how many relevant chunks are retrieved per query

These parameters can be adjusted to trade off between retrieval accuracy, latency, and resource usage.

Safety and Responsible AI Considerations

Ensuring responsible and safe usage of language models is a critical aspect of this project. The system is designed to reduce hallucinations by grounding responses strictly in retrieved document context rather than relying on the language model’s internal knowledge.

To mitigate prompt injection and misuse, the model is instructed to answer only based on the retrieved context and to avoid speculative or out-of-scope responses. Queries that cannot be answered using the available documents are handled conservatively, reducing the risk of generating misleading information.

Additionally, limiting the system to a predefined document domain helps control output relevance and minimizes unintended or harmful responses. These measures collectively contribute to safer and more reliable AI-assisted question answering.

Limitations and Future Work

While the current implementation demonstrates the core concepts of Retrieval-Augmented Generation, it has certain limitations. The system does not yet include quantitative evaluation metrics for retrieval performance, such as precision or recall, and relies primarily on qualitative assessment of answer relevance.

Future improvements may include implementing retrieval evaluation benchmarks, enhancing query preprocessing techniques, experimenting with reranking strategies, and supporting multiple document formats. Additionally, incorporating user feedback loops and source attribution could further improve transparency and trustworthiness.

Conclusion

This project illustrates how Retrieval-Augmented Generation can be used to build a domain-specific question answering system that produces grounded, context-aware responses. By combining document retrieval with controlled generation, the system addresses key limitations of standalone language models, such as hallucinations and lack of domain knowledge.

The work demonstrates foundational RAG concepts including chunking with overlap, embedding-based similarity search, and responsible prompt design. This approach provides a strong foundation for building reliable, scalable, and trustworthy AI-powered information systems.

🔍 Project Overview

This project is a Retrieval-Augmented Generation (RAG) assistant developed as part of Module 1: Foundations of Agentic AI in the Agentic AI Developer Certification (AAIDC) by Ready Tensor.

The assistant answers user questions by retrieving relevant information from a custom document set stored in a vector database and generating grounded responses using Google Gemini.

🧠 System Architecture

User Query
   ↓
Retriever (Chroma Vector Database)
   ↓
Relevant Document Chunks
   ↓
Prompt + Context
   ↓
Gemini LLM
   ↓
Final Answer

🛠️ Technologies Used

• Python
• LangChain (LCEL – LangChain Expression Language)
• langchain-google-genai
• Chroma Vector Database
• Google Gemini (gemini-2.5-flash)
• GitHub Codespaces

📂 Project Structure

AAIDC-Module1-RAG-Gemini/
├── main.py
├── ingest.py
├── data/
│   └── docs.txt
├── requirements.txt
├── .env.example
└── README.md

⚙️ Setup Instructions

1️⃣ Clone the Repository

git clone https://github.com/your-username/AAIDC-Module1-RAG-Gemini.git
cd AAIDC-Module1-RAG-Gemini

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Environment Variable Configuration

🔐 GitHub Codespaces (Recommended)

Add a Codespaces Secret:

• Name: GEMINI_API_KEY
• Value: your Gemini API key

Restart the Codespace after adding the secret.

🖥️ Local Setup

Create a .env file:

GEMINI_API_KEY=your_api_key_here

▶️ Running the Project

Step 1: Ingest Documents

python ingest.py

Step 2: Run the RAG Assistant

python main.py

Type exit to stop the assistant.

💬 Example Usage

You: What is RAG?
Bot: RAG stands for Retrieval-Augmented Generation. It combines document retrieval with language models to generate grounded responses.

You: What is LangChain?
Bot: LangChain is a framework for building applications powered by language models, including tools for retrieval, memory, and agents.

📌 Limitations

• Uses a small static document dataset
• No reranking or retrieval evaluation
• No persistent conversational memory

🚀 Future Improvements

• Add conversational memory
• Expand document corpus
• Add retrieval evaluation and reranking
• Introduce logging and observability

🎓 Certification Context

This project fulfills the requirements for AAIDC Module 1: Foundations of Agentic AI by demonstrating:

• Retrieval-Augmented Generation (RAG)
• Vector database integration
• LLM-grounded question answering
• Clean, secure, and reproducible implementation

🧾 License

This project is intended for educational purposes as part of the Ready Tensor Agentic AI Developer Certification program.