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30 Days of LLMs: Day 11 — Crafting Complex Level 5 Prompts for GPT-4

On Day 11 of our 30 Days of LLMs, we're delving into advanced prompt engineering with GPT-4. Create realistic user questions and learn response evaluation. Enroll now to advance Your Expertise.
Created on December 8|Last edited on December 10
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Day 11: Crafting Complex Level 5 Prompts for GPT-4

On Day 11 of our 30 Days of LLMs, we're delving into the realm of expert prompt engineering with Level 5 prompts in this essential chapter of our complimentary course, "Building LLM-Powered Apps," presented by Weights & Biases. Accompany Darek Kleczek, our experienced machine learning engineer, as he leads you through the development of elaborate, nuanced prompts and their effective use with GPT-4.


Chapter Highlights

  • In-Depth Structure of Level 5 Prompts: Discover the complex architecture of Level 5 prompts, encompassing an overarching goal, specific subtasks, clear output guidelines, and evaluation criteria.
  • Development of Synthetic Datasets: Acquire skills to create artificial datasets for user queries in apps like Wandbot, applying sophisticated prompting methods.
  • Utilizing GPT-4: Observe the utilization of the advanced GPT-4 model in crafting prompts that produce believable, solvable user inquiries.
  • Assessment of LLM Outputs: Learn techniques for parsing and critically assessing LLM responses to ensure they adhere to standards of authenticity and relevance.
  • Effective Data Management: Grasp the methods of recording and versioning your data using the artefact system of Weights & Biases for subsequent analyses and integration into workflows.

Key Course Information

  • No deep machine learning knowledge is needed, just some familiarity with Python programming.
  • Strategies for continual enhancement of your LLM applications.
  • Unique perspectives on the LLM tools used by Weights & Biases.

Free Enrollment

Embark on this educational adventure to excel in crafting LLM-powered applications. Sign up now.

Preview of the Next Chapter

Join us in our next chapter, where we explore the foundational structure of an LLM application.





















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