This repository contains the Generative AI Whitepaper, which explores the implementation of Generative AI in enterprise environments. The paper covers key advancements in Retrieval-Augmented Generation (RAG), Agentic AI, and Large Language Model (LLM) optimization, helping businesses integrate AI-driven automation and decision-making.
- Evolution of AI in Enterprises: Transition from rule-based systems to deep learning and Generative AI.
- Enterprise AI & its Growing Significance: Impact on automation, decision-making, and scalability.
- Retrieval-Augmented Generation (RAG): Enhancing AI contextual awareness and accuracy.
- Fine-Tuning & Optimization: Efficient training techniques like LoRA, QLoRA, and CURLoRA.
- Challenges & Future Directions: Addressing AI bias, security risks, and computational costs.
✔ Co-Pilot AI vs. Traditional Chatbots – Why modern AI-driven assistants are superior.
✔ How RAG Works – Preventing AI hallucinations with real-time knowledge retrieval.
✔ Optimization Techniques – Reducing costs with parameter-efficient fine-tuning.
✔ Enterprise Adoption Strategy – Best practices for scalable AI deployment.