portfolio categories: AI/ML

Prompt engineering is a critical aspect of working with large language models (LLMs) like GPT-3, GPT-4, and other generative AI systems. It involves designing effective prompts to get desired outputs from these models. While prompt engineering has made significant strides in improving AI outputs, there are still several challenges that developers and researchers face. Here’s a breakdown of the challenges and potential solutions:

Natural Language Processing (NLP) is a rapidly advancing field in artificial intelligence (AI) that focuses on enabling machines to understand, interpret, and generate human language. However, there are a number of challenges that arise when developing NLP systems. Below are some of the key challenges in NLP and the corresponding solutions:

Developing AI chatbots is a rapidly evolving field, and while the technology offers significant potential for automating communication and customer support, there are numerous challenges associated with designing, implementing, and maintaining effective chatbot systems. Below is a list of the common challenges in AI chatbot development, along with potential solutions:

Retrieval-Augmented Generation (RAG) in the context of generative AI offers powerful benefits by combining the strength of large language models (LLMs) with external information retrieval systems. However, the use of RAG in generative AI brings its own unique set of challenges. Here’s a breakdown of the challenges and potential solutions:

Retrieval-Augmented Generation (RAG) models, which combine the power of large language models (LLMs) with external retrieval systems to generate more accurate and relevant responses, face several challenges. Here are some key challenges and possible solutions: