1. Abstract

Hallucination-Aware Domain Chatbot is a Generative AI project designed to provide reliable, context-aware answers within a specific domain, such as healthcare information, education, finance, or customer support. Traditional AI chatbots may generate incorrect or unsupported information, commonly known as hallucinations. This project combines Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to generate answers grounded in verified domain-specific documents. The system retrieves relevant information from a knowledge base, supplies the retrieved context to the language model, and generates responses supported by the available sources. It also incorporates confidence assessment, source citations, and fallback mechanisms to identify uncertain queries and avoid unsupported answers. A web-based interface allows users to ask questions, review responses, and access supporting references, improving transparency and trust in AI-powered conversations.

2. Objectives

1. Develop a domain-specific chatbot using Generative AI and LLMs.
2. Reduce unsupported or inaccurate responses generated by language models.
3. Build a searchable knowledge base using trusted domain documents.
4. Retrieve relevant information before generating answers.
5. Provide source references to support chatbot responses.
6. Detect questions that cannot be answered reliably from available information.
7. Implement fallback responses when sufficient evidence is unavailable.
8. Evaluate answer accuracy, relevance, and grounding.
9. Provide a user-friendly conversational interface.

3. Existing System

Traditional chatbots rely on predefined rules, frequently asked questions, or language models that generate responses from learned patterns. Although these systems can provide fluent answers, they may struggle with specialized domain information, outdated knowledge, ambiguous questions, and queries outside their training data.

Disadvantages of the Existing System

1. May generate unsupported or factually incorrect answers.
2. Limited transparency regarding the source of information.
3. Difficulty handling domain-specific queries accurately.
4. May provide confident responses even when evidence is insufficient.
5. Limited ability to recognize missing information.
6. Difficult to evaluate the factual grounding of generated responses.

4. Proposed System

The proposed system combines a Large Language Model with Retrieval-Augmented Generation (RAG), source attribution, and uncertainty-handling mechanisms. When a user submits a question, the system searches a domain-specific knowledge base for relevant passages. The retrieved evidence is provided to the language model, which generates an answer based on the available context.

A grounding-validation module checks whether the answer is adequately supported by the retrieved passages. If relevant evidence cannot be found or the answer cannot be sufficiently verified, the chatbot can ask for clarification or state that it does not have enough information to answer reliably.

Key Features

1. Domain-specific conversational AI.
2. Retrieval-Augmented Generation (RAG).
3. Document-based knowledge retrieval.
4. Source citations and supporting passages.
5. Hallucination and answer-grounding checks.
6. Uncertainty-aware fallback responses.
7. Question-answer history.
8. Dashboard for document management and chatbot interaction.
9. Evaluation of factual accuracy and retrieval quality.

Important note: Hallucination detection is not perfect. A confidence score alone does not prove that an answer is correct. Source verification and testing against carefully prepared questions are essential.

5. Implementation Procedure

1. Domain Selection: Select a specific domain, such as education, company policies, or technical support.
2. Knowledge Collection: Gather reliable documents, PDFs, FAQs, manuals, and other approved domain resources.
3. Document Processing: Extract text from the collected files using PyMuPDF or other suitable document-processing libraries.
4. Text Chunking: Divide documents into smaller passages while preserving useful context and metadata.
5. Embedding Generation: Convert text chunks into vector representations using an embedding model.
6. Vector Database Setup: Store embeddings and document metadata in a vector database such as FAISS or Chroma.
7. Question Processing: Accept user questions and convert them into embeddings for semantic retrieval.
8. Context Retrieval: Retrieve the most relevant passages using similarity search and, optionally, reranking.
9. Answer Generation: Supply the retrieved context and user question to an LLM with instructions to answer only when sufficient evidence is available.
10. Grounding Validation: Check whether key answer claims are supported by the retrieved evidence, using rule-based checks, a separate evaluator, or another suitable verification method.
11. Uncertainty Handling: Return an appropriate fallback response when relevant evidence is missing, conflicting, or insufficient.
12. Citation Generation: Display source document names and relevant passages alongside the generated answer.
13. Dashboard Development: Build the chatbot interface using Streamlit or Flask.
14. Performance Evaluation: Measure retrieval precision, answer correctness, citation accuracy, groundedness, and appropriate abstention on unanswerable questions.
15. Final Testing: Test ambiguous questions, out-of-domain queries, conflicting documents, and questions with no supporting evidence.

6. Software Requirements

1. Programming Language: Python
2. Development Environment: VS Code, PyCharm, or Jupyter Notebook
3. LLM Framework: Hugging Face Transformers or a compatible LLM API
4. RAG Framework: LangChain or LlamaIndex
5. Embedding Model: Sentence Transformers or another suitable embedding model
6. Vector Database: FAISS or Chroma
7. Document Processing: PyMuPDF or pdfplumber
8. Data Processing: NumPy, Pandas
9. Backend: Flask or FastAPI
10. Frontend: Streamlit
11. Database: SQLite or PostgreSQL, if conversation or document metadata storage is required
12. Operating System: Windows or Linux

7. Hardware Requirements

1. Processor: Intel Core i5 or equivalent and above
2. RAM: Minimum 8 GB; 16 GB recommended
3. Storage: At least 10–20 GB of free space, depending on document and model size
4. GPU: Optional when using a hosted LLM API; recommended for local model inference
5. VRAM: Depends on the selected local LLM and quantization configuration
6. Internet Connection: Required for hosted model APIs, package installation, and downloading models

8. Advantages of the Project

1. Reduces unsupported answers by grounding responses in domain documents.
2. Improves transparency through source citations.
3. Supports specialized knowledge beyond a model's general training.
4. Can acknowledge when evidence is insufficient.
5. Helps users verify answers against source material.
6. Supports domain-specific document search and question answering.
7. Enables systematic evaluation of factual reliability.
8. Can be adapted to educational, enterprise, and technical-support applications.