ChatGPT prompt engineering for developers
PromptA short course by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI).
Capabilities5 decomposed
effective prompt crafting for llms
Medium confidenceThis capability teaches users how to design and structure prompts for large language models (LLMs) to elicit desired responses. It utilizes a combination of practical examples and theoretical frameworks to guide users in understanding the nuances of prompt engineering, focusing on clarity, specificity, and context. The course emphasizes iterative testing and refinement of prompts to improve output quality, making it distinct in its hands-on approach to learning.
The course combines insights from industry leaders and practical exercises, providing a unique blend of theory and application that is not commonly found in other prompt engineering resources.
More comprehensive than typical online tutorials, as it integrates expert insights and structured learning paths.
iterative prompt testing framework
Medium confidenceThis capability provides a structured methodology for testing and refining prompts through iterative cycles. Users are guided to formulate a prompt, evaluate the output, and adjust the prompt based on the model's responses. This approach encourages a systematic exploration of prompt variations, helping users understand the impact of different wording and context on the model's behavior.
Utilizes a feedback loop approach that emphasizes learning from each iteration, which is less common in standard prompt engineering resources.
More structured than ad-hoc testing methods found in other courses, ensuring a comprehensive understanding of prompt dynamics.
contextual prompt enhancement techniques
Medium confidenceThis capability focuses on teaching users how to incorporate context into their prompts to improve the relevance and accuracy of model outputs. It covers techniques such as providing background information, specifying roles, and using examples to guide the model's understanding. By emphasizing the importance of context, this capability helps users craft prompts that align more closely with their intended outcomes.
Emphasizes the role of context in prompt design, providing techniques that are often overlooked in other resources.
More focused on contextual understanding than generic prompt crafting guides.
role-based prompt structuring
Medium confidenceThis capability teaches users how to structure prompts by assigning specific roles to the model, such as 'expert', 'assistant', or 'teacher'. By framing prompts in this way, users can influence the tone and style of the responses generated by the model. The course provides examples and best practices for effectively using role-based prompts to achieve desired outcomes.
Focuses on the innovative use of role assignment to guide model behavior, which is not commonly emphasized in other prompt engineering resources.
Offers a unique perspective on prompt design that is often missing in conventional tutorials.
advanced prompt strategies for specific tasks
Medium confidenceThis capability provides users with advanced strategies for crafting prompts tailored to specific tasks, such as summarization, translation, or question answering. It includes guidelines on how to frame prompts based on the task's requirements and the expected output format. By focusing on task-specific strategies, users can enhance the effectiveness of their prompts significantly.
Provides tailored strategies for various tasks, which are often generalized in other prompt engineering courses.
More focused on task-specific needs than generic prompt crafting resources.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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OpenAI Prompt Engineering Guide
Strategies and tactics for getting better results from large language models.
llm-universe
本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/
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DeepEval
LLM evaluation framework — 14+ metrics, faithfulness/hallucination detection, Pytest integration.
Best For
- ✓developers looking to enhance their interaction with LLMs
- ✓developers and data scientists experimenting with LLM outputs
- ✓developers aiming to create more nuanced interactions with LLMs
- ✓developers looking to customize model interactions
- ✓developers and content creators needing task-specific prompt guidance
Known Limitations
- ⚠The course does not cover advanced model fine-tuning techniques.
- ⚠Requires a basic understanding of LLMs and their behavior.
- ⚠May require multiple iterations to achieve optimal results.
- ⚠Role effectiveness may vary based on the prompt and context.
- ⚠Task complexity may require additional context for optimal results.
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
A short course by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI).
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