Curso de PROMPT ENGINEERING: como CREAR PROMPTS efectivos

Introduction to Principles of Effective Prompts

In this course, we will learn the fundamental principles of creating effective prompts and best practices such as feedback, summarization, transformation, and inference. These practices can be applied to any language model, not just ChatGPT. Let's dive into the two fundamental principles.

Principle 1: Clear and Specific Instructions

• Delimit the prompt between quotation marks or any other separating sign to ensure that the model understands our expectations.

• Example: Asking the model to create a summary of a sentence by specifying that the text is delimited by double quotes.

• Request structured output for easy readability and comprehension.

• Example: Asking for a list of 10 books written in the 18th century with authors and genres presented in a table format.

Principle 2: Give Model Time to Think

• Confirm if conditions are met to verify if the model understood correctly.

• Example: Asking ChatGPT to generate a list of European country capitals and then asking if all capitals are included.

• Offer an example of the expected result to help guide the model.

• Example: Requesting a list of the top 5 science fiction books of all time with each element formatted based on "1984" novel.

Overview of the Results

The speaker discusses how the results generated by the model align with the requested format. The examples provided include novel names, their English versions, authors or publication dates, and brief summaries. The model follows the given format consistently.

Results in Desired Format

• The speaker demonstrates that the model produces results in the requested format.

• Examples show that novel names are provided along with their English versions, author information or publication dates, and concise summaries.

• The model maintains consistency by following the original format provided.

Using Examples for Other Requests

The speaker explains how using examples can help generate results for other book-related requests. By providing an initial example, subsequent results can be obtained based on a similar pattern.

Utilizing Initial Example

• By providing an initial example, subsequent requests for book recommendations can be generated.

• Demonstrates how using one example allows for generating multiple related results.

Consistent Style and Answering Questions

The speaker highlights how GPT models aim to answer questions consistently in a specific style. An example conversation about patience and persistence is used to illustrate this point.

Consistent Style of Responses

• GPT models strive to answer questions in a consistent style.

• An example conversation about patience and persistence showcases how the model responds while maintaining a consistent tone.

• The model provides definitions of patience and persistence while highlighting their differences.

Tasking GPT to Answer Questions

This section focuses on instructing GPT to respond to specific questions in a consistent style. An example question about resilience is used to demonstrate this process.

Instructing GPT to Answer Questions

• The task for GPT is to answer questions in a consistent style.

• An example question about resilience is given, and the model responds by relating it to playing a game and sometimes losing.

• The model provides insights into what resilience entails, emphasizing the mindset required.

Clear and Specific Instructions

This section emphasizes the importance of providing clear and specific instructions to GPT models. It also concludes the first principle of writing clear instructions.

Writing Clear Instructions

• Providing clear and specific instructions is crucial when working with GPT models.

• The example conversation about resilience demonstrates how the model follows the given style from previous responses.

• By following clear instructions, subsequent questions can be answered in a consistent manner.

Giving Time for Model Thinking

This section introduces the second principle of giving time for the model to think. Two specific tactics are discussed, including specifying steps for completing a task.

Allowing Time for Model Thinking

• The second principle involves giving time for the model to think before responding.

• Two tactics are presented:

• Specifying steps for completing a task that may require multiple steps.

• Presenting more complex examples that incorporate all previously discussed tactics.

Example Incorporating Tactics

An example prompt is used to demonstrate how all previously discussed tactics can be applied together.

Example Prompt Incorporating Tactics

• A more complex example prompt is introduced that incorporates all previously discussed tactics.

• Steps include summarizing quoted text in one sentence, translating it into Spanish, listing names mentioned in the summary, and formatting it as requested.

• The model successfully executes all steps according to the given format.

Different Ways of Summarizing

This section explores different ways of summarizing text, including summarization, condensation, and creating an excerpt. The same text is used to demonstrate the different results obtained.

Different Approaches to Summarizing

• Three different approaches to summarizing are discussed: summarization, condensation, and creating an excerpt.

• The same text is used as an example to showcase how each approach yields distinct results.

• Summaries can vary in length and level of detail depending on the chosen approach.

Recommendations for Practice

The speaker encourages learners to practice by experimenting with the provided programs. Two tactics are reiterated, emphasizing the importance of evaluation and modification.

Practicing with Provided Programs

• Learners are encouraged to practice using the provided programs mentioned throughout the course.

• Experimenting with these programs allows for hands-on experience and application of learned concepts.

• Evaluation and modification are key aspects when working with GPT models.

Asking Model to Verify Results

This section focuses on asking the model to verify its own results. An example equation is used to illustrate this tactic.

Asking Model for Verification

• Asking the model to verify its own results can be useful, particularly in mathematical operations.

• An example equation is given where the model is asked if a statement is true or false without performing further operations.

• The model's response demonstrates that it quickly answers based on initial information without executing additional calculations.

Requesting Review of Results

This section introduces a variation of asking the model to review its results by solving an equation before responding. A comparison between two responses showcases how verification can lead to more accurate answers.

Requesting Review of Results

• A variation of asking the model to review its results is introduced by requesting it to solve an equation before responding.

• A comparison between two responses, one without reviewing and one with reviewing, demonstrates how verification leads to more accurate answers.

Best Practices for Generating Prompts

This section presents best practices for generating prompts. The importance of feedback and modifying prompts is emphasized.

Feedback and Modification

• Evaluating the results obtained from prompts is crucial in improving their accuracy.

• Modifying prompts based on feedback allows for refining the desired outcome.

• Even well-developed prompts can be further improved or adjusted to achieve better results.

Different Approaches to Summarization

This section discusses different approaches to summarization, including summarizing, condensing, and creating an excerpt.

Different Approaches to Summarization

• Various approaches to summarization are explored, such as summarizing, condensing, and creating an excerpt.

• The speaker emphasizes that each approach yields different results.

Understanding Different Types of Summaries

In this section, the speaker discusses the different types of summaries and their varying levels of detail.

Types of Summaries

• A compendium provides a more comprehensive summary that includes sections from the original text.

• An extract offers a more detailed summary than a compendium, including additional information not present in the original text.

• A concise summary focuses on a specific word count or number of words, allowing for a brief overview.

Importance of Specific Instructions

• It is crucial to provide specific instructions when requesting a summary to ensure desired results.

• By specifying the type of summary and any word count limitations, one can obtain more accurate results.

The Practice of Iteration

This section emphasizes the importance of iteration in improving prompt engineering.

The Process of Iteration

• Iteration involves continuously refining prompts until desired outcomes are achieved.

• Feedback plays a vital role in improving prompts and achieving desired results.

The Power of Inference

This section explores how chat GPT can infer information beyond what is explicitly provided in the prompt.

Understanding Inference

• Inference refers to utilizing available information to draw conclusions or deductions.

• Chat GPT has the ability to infer various aspects such as the main theme, tone, author's perspective, purpose, target audience, historical context, and effects of arguments presented in a text.

• Inference is particularly useful for analyzing competitor texts, product reviews, and understanding the target audience.

Practical Example: Extracting Insights from Amazon Product Reviews

This section provides a practical example of using chat GPT to extract insights from Amazon product reviews.

Extracting Insights from Reviews

• By providing a prompt with specific instructions, such as determining the overall sentiment, identifying positive and negative opinions, highlighting main advantages and disadvantages, and presenting the information in a list format, chat GPT can extract valuable insights from product reviews.

• The example demonstrates how chat GPT can analyze multiple reviews to determine sentiment, quantity of positive/negative opinions, main advantages and disadvantages of a product.

Recap and Next Steps

This section summarizes the key takeaways from the course on prompt engineering.

Key Takeaways

• Prompt engineering involves applying various techniques learned throughout the course to obtain desired results.

• Iteration and feedback are essential for refining prompts until desired outcomes are achieved.

• Chat GPT has impressive inference capabilities that allow it to extract relevant information beyond what is explicitly provided in the prompt.

• To further enhance prompt engineering skills, it is recommended to explore other parts of the course and continue learning about chat GPT's capabilities.