AI-Powered Internal Policy Chatbot using LLM & RAG

Background

As organizations grow, employees often struggle to find quick answers to internal policy questions such as HR policies, IT security guidelines, leave rules, and travel reimbursement procedures. These documents are usually long, stored in various formats, and not easily searchable. To address this challenge, a Proof of Concept (POC) was initiated to build an AI-powered chatbot using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). The goal was to help employees retrieve accurate, contextual information through a natural language interface.

Challenge

• Employees had no easy way to access policy information quickly.

• Manual searching through documents was time-consuming and inefficient.

• HR and Admin teams received repetitive queries, increasing their workload.

• The solution had to ensure data privacy and avoid exposing internal content to third-party services.

Solution

Document Ingestion & Indexing

• Supported file formats: PDF, DOCX, TXT

• Documents were chunked using paragraph-based logic and embedded using Sentence Transformers.

• Embeddings were stored in a local FAISS vector database for fast retrieval.

LLM Integration with RAG

• Integrated OpenAI GPT-4 via LangChain for orchestration.

• Used Retrieval-Augmented Generation to pull relevant content from the vector store and ground the LLM’s response.

• All responses included references to source documents and sections for transparency.

Query Interface

• Developed a simple, responsive web UI using React.

• Allowed users to input questions and view clear, contextual responses with source citations.

Security & Admin Features

• POC included dummy login with optional SSO for future integration.

• Local document and embedding storage ensured data privacy.

• Optional admin module allowed document uploads and usage monitoring.

Outcome

Sample Use Cases:

• “What is the company’s policy on sick leave?”
  Response: “Employees are entitled to 12 paid sick leave days per year.” [HR_Policy.pdf, Section 3.2]

• “Can I carry forward unused vacation days?”
  Response: “Up to 5 days may be carried forward.” [Leave_Policy.docx, Clause 4.1]

• “What’s the procedure for reporting a phishing email?”
  Response: “Forward the email to infosec@company.com and delete it.” [IT_Security_Guidelines.pdf, Page 8]

Conclusion

The POC proved the effectiveness of using LLMs and RAG for answering internal policy-related queries. The chatbot successfully delivered high-accuracy answers with contextual grounding and source transparency. It demonstrated potential to reduce HR and Admin workloads and improve employee satisfaction through better access to information.