The Prompt Secrets That Leave ChatGPT Looking Confused

1. Introduction to Industrial Engineering

Google has rolled out a guide to Prompt engineering — and now even your grandma can squeeze masterpieces out of ChatGPT.

Google Blows Up BYA: Prompt Engineering Secrets That Make ChatGPT Cry in the Corner

Key components:

  • Clear instructions: Defining the task without ambiguity.
  • Contextualization: Providing background information.
  • Formatting: Using templates, separators, and labels.
  • Iterations: Optimizing queries step by step based on model responses.

2. Basic methods

Zero-Shot Prompting

Query without examples.

Example: “Generate a summary of the article.”

Few-Shot Prompting

Request with examples to demonstrate the expected format.

Example: *Entrance: “AI in medicine: prospects and risks.

“Exit: “The article discusses the use of AI for diagnostics…”

3. Advanced Context Management Techniques

System Prompt

Setting the general role of the model.

Example: “You are a technical writer specializing in AI.”

Contextual Prompt

Clarification of the task within the framework of previous interactions.

Example: “Based on the discussion above, suggest three ways to improve the code.”

Role Prompt

Assigning a model to a specific role.

Example: “Answer as a senior developer with experience in Python and machine learning.”

4. Advanced Strategies

Chain of Thought (CoT) Step-by-step reasoning for complex problems.

Example: “Explain the solution to the problem step by step: What is the period of revolution of Mars around the Sun?”

ReAct Framework Integrate reasoning and actions with external tools.

Example: “Calculate the project price using the current ETH rate. [Action: CoinGecko API request].”

5. Setting up generation parameters

Temperature

  • Low values ​​(0.1–0.3): Deterministic responses.
  • High values ​​(0.7–1.0): Creative and varied conclusions.

Top-K and Top-P

  • Top-K=50: Limit the selection to the 50 most probable tokens.
  • Top-P=0.9: Dynamic token selection based on cumulative probability.

6. Best practices

  1. Use structured examples: 3-5 examples increase accuracy by 40% (Google data).
  2. Experiment with formats: JSON for structured data. Bulleted lists for comparisons.
  3. Control the length of the output: “Keep your answer to 100 words.”
  4. Avoid vague instructions: Incorrect: “Write something interesting.” Correct: “Generate 5 theses on the use of AI in logistics.”

7. Application outside the text

Code generation

Example request: “Write a Python function to parse CSV files.”

Data Analysis

Example request: “Visualize the data from the attached DataFrame as a graph.”

Multilingual support

Example request: “Translate technical documentation from English to Chinese.”

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