The Evolution of Personalization and Context in Generative AI

Generative AI has rapidly evolved from handling generalized tasks to enabling highly personalized and context-aware systems. Early large language models, while powerful, often struggled to deliver outputs tailored to specific users or use cases due to limited contextual understanding and lack of personalization.

In this session, we will explore how advancements such as prompt engineering, retrieval-augmented generation (RAG), and fine-tuning are enabling more effective and specialized AI systems. We’ll also dive into how long-context LLMs and unified context architectures are improving coherence, reducing hallucinations, and transforming AI into intelligent, decision-making agents.

This is a practically relevant and insight-driven session designed to help practitioners understand how to build more personalized, reliable, and context-aware GenAI systems .

Key Takeaways:

• Evolution of Generative AI – from generalized models to personalized and context-aware systems
• Personalization Techniques – understanding prompt engineering, RAG, and fine-tuning for tailored outputs.
• Context-Aware Systems – how long-context LLMs improve coherence and reduce hallucinations
• AI Agents in Practice – enabling workforce automation through specialized AI systems
• System Design Perspective – building scalable and reliable context-driven GenAI applications

Saptarshi Pani is a Founding Member and Chief Data and Research Scientist at Alchemyst AI, specializing in Generative AI, Large Language Models, and Computer Vision.

He has a strong research background with multiple publications and focuses on building scalable, real-world AI systems, including agentic frameworks and context-aware LLMs. He has led the development of AI products and contributed to globally recognized startups, working at the intersection of research and applied AI.