Pluralsight – Prompt Engineering for Improved Performance

Pluralsight – Prompt Engineering for Improved Performance
English | Tutorial | Size: 486.38 MB

Learn advanced prompt engineering techniques to enhance language model performance. Explore optimization techniques and gain insights into factors affecting latency and performance. Guide models to generate accurate, coherent, and relevant outputs.

What you’ll Learn:
Advanced prompt engineering techniques can significantly enhance the performance of language models by guiding them to generate more accurate, coherent, and relevant outputs. In this course, Prompt Engineering for Improved Performance, you’ll gain the ability to harness the capabilities of language models effectively, ensuring they align with specific use cases, minimize biases, and deliver high-quality outputs.

First, you’ll journey beyond the realm of basic prompting techniques and learn how to leverage advanced prompting methods that elevate a model’s performance on reasoning tasks.

Next, you’ll discover the art of prompt optimization for specific use cases by diving into optimization techniques for summarizing and expanding text, text-to-image generation, inferring sentiment, and transforming text from one format to another.

Finally, you’ll gain insights into the key factors that impact the latency and performance of text generation models and explore mitigation techniques to improve performance.

When you finish this course, you’ll have the knowledge of advanced prompt engineering techniques needed to harness the capabilities of language models effectively, ensuring they align with specific use cases, minimize biases, and deliver high-quality outputs.

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