The advent of Generative AI has sparked a wave of enthusiasm among businesses eager to harness its potential for creating Chatbots, companions, and copilots designed to unlock insights from vast datasets. This journey often begins with the art of prompt engineering, which presents itself in various forms, including Single-shot, Few-shot, and Chain of Thought methodologies. Initially, companies tend to deploy internal chatbots to bolster employee productivity by facilitating access to critical insights. Furthermore, customer support, traditionally seen as a cost center, has become a focal point for optimization efforts, leading to the development of Retrieval Augmented Generation (RAG) systems intended to provide deeper insights. However, challenges such as potential inaccuracies or "hallucinations" in responses generated by these RAG systems can significantly impact customer service representatives' decision-making, potentially resulting in customer dissatisfaction. A notable incident involving Air Canada has recently highlighted the potential risks to brand reputation and financial stability posed by deploying these autonomous chatbots in customer support scenarios. The prospect of creating similar chatbots for financial advisors, capable of delivering human-like yet fundamentally flawed responses, raises significant concerns. Issues related to quality (such as hallucination, truth grounding, and comprehensiveness), content safety, and the risk of intellectual property leakage are among the key hurdles preventing many generative AI applications from reaching production stages.
It is easy to build a simple RAG system by combining Vector search for retrieval and LLM to summarize retrieved chunks, a massive upgrade from traditional knowledge bases with a limited understanding of the semantic nature of questions. These systems show poor performance in the real world for a multipart of complex questions.
Prompt engineering techniques such as Chain of Thoughts (CoT) involve generating intermediate steps or reasoning paths when solving complex problems, especially in language models. It's like showing one's work in math problems but applied to AI. The model explicitly generates a sequence of thoughts or reasoning steps before arriving at a final answer or conclusion. Although CoT excels at breaking down complex tasks or questions, their effectiveness hinges on the context provided if used in RAG systems.
The ReACT (Synergizing Reasoning and Acting in Language Models) paper shows how this approach is far superior to CoTs. Let's look into the basics. In the study of autonomous agents and multi-agent systems, the concepts of Thought, Action, and Observation play crucial roles in defining how these agents perceive, interpret, and interact with their environment.
Together, Thought, Action, and Observation form a cyclical process that enables AI agents to operate autonomously, learn from their environment, and achieve their goals.
Agentic workflows, also known as Agents, harness the capabilities of Large Language Models (LLMs) to navigate the complexities of constructing intricate Retrieval Augmented Generation (RAG) systems. They adeptly segment elaborate tasks into manageable sub-tasks, utilize external systems to enhance their knowledge base, and monitor the outcomes to determine subsequent actions, ensuring the initial query's goals are met. The following provides a standard depiction of how a RAG system incorporates external resources for knowledge expansion.
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The ReAct agent represents an advanced form of artificial intelligence, drawing inspiration from the human processes of thinking, acting, and observing to tackle challenges methodically. Whether you're a Generative AI aficionado or looking to gain a competitive edge by creating production-level agents through an intuitive visual platform, the Karini AI platform is designed to accelerate your journey to market with ethical AI solutions.
About the Author
Deepali Rajale is a founder of Karini AI with a mission to democratize generative AI across enterprises. She enjoys blogging about Generative AI, coaching customers to optimize Generative AI practice. She loves to spend time outdoors camping with her family and also a poet and has published a book.
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