Anant Agarwal

Large Language Models (LLMs) have revolutionized text generation and comprehension tasks. However, their ability to perform logical reasoning – a crucial aspect of human intelligence – remains challenging. This session delves into Prompt Engineering, a powerful technique for unlocking the logical reasoning potential of LLMs. We will explore a diverse set of prompt engineering techniques, each designed to guide LLMs towards more robust reasoning, such as Chain-of-Thought Prompting, Least-to-Most Prompting, Decomposed Prompting, Interleaved Retrieval with CoT Prompting, Successive Prompting, Step-Back Prompting, Multi-Agent Prompting among others. We will leverage the LangChain framework in Python to demonstrate the practical implementation of these techniques. By examining the effectiveness of these techniques across various reasoning tasks, this session will equip you with the knowledge and tools to unlock the true potential of LLMs in logical reasoning.