From the course: Agentic AI for Developers: Concepts and Application for Enterprises

Basic GenAI uses and limitations

From the course: Agentic AI for Developers: Concepts and Application for Enterprises

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Basic GenAI uses and limitations

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- [Instructor] Let's get started with the course. In this chapter, we will introduce the concept of agents with an example. We begin with a review of generative AI. In order to differentiate from agentic AI, we will call the use of LLMs to generate content as basic GenAI. Generative AI, or GenAI for short, has taken the world by storm recently. Using natural language prompts to drive models and generate content is creating tremendous interest across individuals and enterprises. Business applications and processes are changing to accommodate GenAI to improve efficiency. What are some of the popular use cases of basic GenAI, which is prompting the LLM for content and answers? Question answering or search has been the most popular use case with retrieval augmented generation or RAG implementations. Creating content, whether it's software code or marketing material, is also becoming popular. GenAI is able to personalize content and offers based on user profiles and inputs. Language translation and interpretation is helping humans break the language communication barrier. GenAI is helping in diagnosis of symptoms in healthcare. It is also playing a role in security applications, helping detect complex threats and triage security vulnerabilities. There are then the known limitations of GenAI. Basic GenAI has not been used for autonomous decision making and actions. It is merely used as a helper or cohort. There are limitations in goal-oriented behavior, especially when the goal requires a task breakdown process and workflow execution. Hallucinations have limited the use of GenAI in critical applications requiring human intervention. Finally, ethics and bias are key considerations when using GenAI. Agentic AI is the next revolution in GenAI that helps overcome some of the key limitations and help expand the use of GenAI to more industrial grade applications.

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