🎓CURSO PROMPT ENGINEERING en Español - GRATIS - 🤖CLASE 05- STANDAR PROMPT VS CHAIN OF THOUGHT

Introduction to Prompt Engineering

Overview of Prompt Types

• Joaquín Barberá introduces the topic of prompt engineering, focusing on different types of prompts.

• He distinguishes between two main categories: standard prompts and chain-of-thought (COT) prompts.

Standard Prompts vs. Chain-of-Thought Prompts

• Standard prompts do not generate a reasoning process; they provide direct answers without showing the steps taken to arrive at those answers.

• In contrast, chain-of-thought prompts require the AI model to produce a reasoning path that leads to its answer.

Generating Chain-of-Thought Prompts

Methods for Inducing COT Responses

• There are two ways to instruct the model to generate a chain of thought:

• Directly adding phrases like "solve the problem step by step" at the end of the prompt.

• Providing examples within the prompt that illustrate logical processes.

Examples Demonstrating Prompt Types

Example 1: Identifying a City

• The presenter sets up an example with clues about a city in Spain, specifically Murcia.

• The first attempt using a standard prompt results in an incorrect answer ("Madrid").

• By modifying it into a chain-of-thought prompt, he successfully guides the model through identifying Spain, Murcia, and ultimately Cartagena as the correct answer.

Example 2: Solving a Math Problem

• A mathematical problem is presented where Juan has tennis balls. Using a standard prompt yields an incorrect total (13).

• When reformulated as a chain-of-thought prompt with explicit instructions for step-by-step resolution, it correctly calculates Juan's total as 16 tennis balls.

Conclusion on Effectiveness of COT Prompts

Summary of Findings

Understanding AI Text Generation

The Importance of Logical and Mathematical Writing

• The relevance of using a logical or mathematical approach in writing is emphasized, suggesting that it holds greater importance than other styles.

• A distinction is made between the performance of AI models with the "pro" standard versus the "change of out" model, indicating that the latter performs better in generating text.

• It is explained that AI models generate text by predicting the next most probable word, which can lead to errors when selecting tokens from a broader context.

Step-by-Step Problem Solving

• The discussion compares human problem-solving methods to those of AI, highlighting that both benefit from breaking down tasks into smaller steps for better accuracy.