// LLM & Knowledge Systems
Enterprise RAG Platform
An enterprise knowledge platform combining semantic search, vector retrieval, prompt engineering, and internal document integration.
Role
Artificial Intelligence Engineer
Duration
Aug 2024 - Present
Project Preview
Enterprise RAG Platform
// Project overview
The Project
This platform enables users to ask natural-language questions across internal company documents. It retrieves relevant information from a vector database and generates grounded, context-aware answers using a large language model.
The Challenge
Searching large volumes of unstructured enterprise documents.
Reducing hallucinations in generated responses.
Maintaining low-latency semantic retrieval.
Integrating multiple internal knowledge sources.
The Solution
Created document ingestion, cleaning, and chunking pipelines.
Generated embeddings and stored them in a vector database.
Implemented semantic retrieval and prompt-context generation.
Built FastAPI microservices for scalable real-time inference.
// Core capabilities
Key Features
Document ingestion
Semantic search
Context-aware answers
Source-based retrieval
Prompt engineering
API integrations
// System architecture
How It Works
Documents are uploaded or synchronized from internal sources.
Text is cleaned, divided into chunks, and converted into embeddings.
Embeddings are stored in a vector database.
The user submits a natural-language question.
Relevant document chunks are retrieved using semantic similarity.
The LLM generates an answer using the retrieved context.
// Project impact
Results
Reduced time spent manually searching internal documentation.
Improved answer relevance through retrieval-based context.
Created a scalable architecture for adding new knowledge sources.