Research

At AGAI, we harness the transformative potential of Generative AI across multiple domains. Our work is grounded in rigorous methodologies and spans a diverse set of themes, including:

• Retrieval-Augmented Generation (RAG): Enhancing language models by integrating external knowledge bases for improved accuracy and context-aware responses.
• Large Language Model (LLM) Applications: Innovating practical applications of LLMs in areas such as healthcare, education, cybersecurity, creative arts, and business analytics.
• Synthetic Data Generation: Producing high-quality synthetic datasets to enable robust AI model training and overcome data scarcity challenges.
• Domain-Specific Fine-Tuning: Developing and refining models tailored specifically for targeted industries to maximize relevance and performance.
• Explainability and Transparency: Creating frameworks to interpret and understand AI decision-making processes, thereby fostering trust and responsible AI use.
• Mitigating Hallucinations and Bias: Researching techniques to reduce errors and biases in generative outputs, ensuring reliability and fairness.
  
  Through these explorations, we aim to shape a future where Generative AI empowers transformative innovations, responsibly impacting society and industry alike.