Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices


The advent of Large Language Models (LLMs) such as GPT, Llama, PaLM has revolutionized the AI space and has enabled organizations to reimagine the reinvent the technology and business ecosystem. These models are helping to create unique capabilities, be it enterprise search, topic identification, summarization, conversational bots, content generation and many more. Organizations are leveraging LLMs through various means such as out of box application, prompt engineering and model fine tuning. Though we are seeing early success, there are challenges and adopting LLMs for various business use cases is still an evolving space. In this talk we delve into the cutting-edge aspects of LLMs, focusing on four critical dimensions: Prompt Engineering, Evaluation, Model Optimization & Deployment, and Responsible AI.

Prompt Engineering involves developing and optimizing prompts to efficiently guide LLMs by exploring various prompting techniques like Chain-of-Thought, Chain-of-Prompts, Tree of Thought, ReACt to generate a reliable response.
The Evaluation dimension addresses the challenge of assessing LLM's performance. Comprehensive evaluation criteria and metrics, such as Perplexity, ROUGE, and human evaluation are covered to measure effectiveness across tasks and domains.

The Model Optimization and Deployment dimension covers the SOTA parameter efficient fine-tuning (PEFT) methods like LoRA/QLoRA that is used to train and deploy LLMs at scale by ensuring low computation usage and cost.
Responsible AI dimension emphasizes the ethical considerations in LLMs deployment thereby creating trust among the consumers of AI applications. Strategies for ensuring security, privacy, interpretability, and fairness are explored with techniques for protecting sensitive information, enhancing trust, and detecting bias.

In summary, this session will comprehensively cover current challenges and solutions across various dimensions for adoption of LLMs at enterprise scale. It will also provide researchers, practitioners, and policymakers with valuable insights for adopting and deploying LLMs effectively and responsibly. Unlocking the vast potential of LLMs in NLP will trigger innovative ideas and is expected to disrupt and radically transform the technology and business landscape in the coming days.


Rohit Sroch is a Sr. AI Scientist at Artificial Intelligence Labs at Course5 Intelligence, with over 5 years of experience in the Natural Language Processing and Speech domains. He plays a pivotal role in conceptualizing and developing AI systems for the Course5 Products division. Simultaneously, he maintains an active involvement in his research endeavors, leading to the publication of several research papers in recent years. Also, his fervent interest in the constantly evolving landscape of AI drives him to engage in continuous research and stay abreast of the latest technologies.

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