Textual Intelligence

10From Prompts to Performance: Innovations in Context Learning

Amandeep Sharma*, Prince Kumar and Shashank Dhamija

Expert Insights from GEN AI Architects Specializing in Cross-Industry Applications, Collaborating with Industry Leaders in Retail, Supply Chain and Logistics, Sustainability, Healthcare, and Finance, Gurugram, Haryana, India

Abstract

One essential component of using language models to produce targeted and desired results is prompt engineering. This technique entails creating clear and efficient cues, or prompts, to elicit desired answers from advanced language models. The process of creating successful prompts is intricate and demands a thorough comprehension of language models, their capabilities, and the intended results. By utilizing effective prompt crafting techniques, the caliber and pertinence of the answers derived by language models can be improved, better matching them to the particular objectives and specifications. Also, in generative artificial intelligence (GenAI), careful design, effective training methods, and post-processing are all necessary to maintain control over model behavior and guarantee high-quality output. These methods enable developers and practitioners to exercise control over the behavior and output quality of generative AI models when used ethically and strategically, encouraging responsible and efficient use in a variety of applications. Additionally, Google’s Gemini - large language model (LLM) is shown, which is a strong contender ...