Certifiedunder the RAG-Based AI Assistant module in the Agentic AI Essentials program.Certifiedunder the RAG-Based AI Assistant module in the Agentic AI Essentials program.
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.
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.
The system follows a standard Retrieval-Augmented Generation architecture composed of two primary stages: retrieval and generation.
Document Ingestion
Raw documents are loaded and preprocessed to prepare them for indexing.
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.
Vector Store
The embeddings are stored in a vector database that enables efficient similarity search.
Query Processing and Retrieval
User queries are embedded and compared against stored document vectors to retrieve the most relevant chunks.
Response Generation
Retrieved chunks are passed as context to a large language model, which generates a final answer grounded in the provided documents.
The Retrieval-Augmented Generation pipeline operates as follows:
By separating retrieval from generation, the system ensures that responses remain grounded in the source documents, reducing hallucinations and improving factual accuracy.
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.
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:
These parameters can be adjusted to trade off between retrieval accuracy, latency, and resource usage.
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.
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.
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.
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.
User Query
↓
Retriever (Chroma Vector Database)
↓
Relevant Document Chunks
↓
Prompt + Context
↓
Gemini LLM
↓
Final Answer
AAIDC-Module1-RAG-Gemini/
├── main.py
├── ingest.py
├── data/
│ └── docs.txt
├── requirements.txt
├── .env.example
└── README.md
git clone https://github.com/your-username/AAIDC-Module1-RAG-Gemini.git cd AAIDC-Module1-RAG-Gemini
pip install -r requirements.txt
Add a Codespaces Secret:
Restart the Codespace after adding the secret.
Create a .env file:
GEMINI_API_KEY=your_api_key_here
python ingest.py
python main.py
Type exit to stop the assistant.
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.
This project fulfills the requirements for AAIDC Module 1: Foundations of Agentic AI by demonstrating:
This project is intended for educational purposes as part of the Ready Tensor Agentic AI Developer Certification program.