RAG Content Agent with LLM

Generate domain-specific content from connected knowledge sources using RAG and LLMs through a controllable, review-friendly application interface.

Categories

Tech Used

Problem

Businesses need AI-generated content that reflects their own information, follows constraints, and remains easy to review before it is published, shared, or used in an operational workflow.

Approach

• Grounded LLM generation with retrieval from custom knowledge sources and structured contextual data
• Used RAG, embeddings, PostgreSQL, and ChromaDB to provide relevant context for generation
• Added prompt templates, tone controls, and output constraints to improve consistency
• Connected the generation workflow with database-backed content and application logic
• Built a user interface for drafting, reviewing, editing, and exporting generated content
• Prepared API-based integration patterns for embedding the generation workflow inside larger applications

Results

• Delivered more consistent and context-aware LLM generation for domain-specific workflows
• Created a reusable pattern for internal writing assistants, reporting tools, and content-generation applications
• Made RAG-based content generation accessible to non-technical users through a review-oriented interface
• Connected knowledge retrieval, structured data, LLM generation, and UI review in one end-to-end workflow

Demo Videos