Скачать или смотреть Prompt Engineering Guide: Introduction

This guide (https://www.promptingguide.ai/ru/intr...) provides a comprehensive overview of the fundamentals, essential LLM configurations, and best practices needed to craft powerful prompts.

What is Prompt Engineering?

Prompt Engineering is an iterative process that helps researchers and developers understand the capabilities and limitations of LLMs. It involves designing robust and effective methods for interacting with models like GPT-4, LLaMA, Gemini, and Mixtral. By mastering PE skills, you can enhance LLM performance on both general and complex tasks, such as arithmetic reasoning and question-answering systems.

Key LLM Configuration Settings

When interacting with an LLM via API or directly, you can adjust several critical parameters to control the output. We dive deep into:
• Temperature: Controls the randomness of the output. Lower values lead to more deterministic, factual results (recommended for fact-based Q&A), while higher values increase randomness for more diverse or creative outputs (like poetry generation).
• Top_p: Similar to Temperature, this technique (called nucleus sampling) controls how deterministic the model is. It is recommended to alter either Temperature or Top_p, but not both simultaneously.
• Maximum Length: Regulates the number of tokens the model generates, helping prevent overly long or unnecessary responses and controlling costs.
• Stop Sequences: Specific strings that instruct the model to halt token generation, useful for controlling the length and structure of the output (e.g., adding "11" to stop a list at 10 items).
• Frequency Penalty: Punishes tokens based on how often they have already appeared in the prompt and response, reducing repetition of words.
• Presence Penalty: Applies a uniform penalty to all repeating tokens, regardless of how often they appeared (unlike Frequency Penalty). Use a higher presence penalty for diverse or creative text.

Essential Prompt Elements and Design Tips

A well-composed prompt can contain several elements: an Instruction (the specific task), Context (external information to guide the model), Input Data (the question or input to be processed), and an Output Indicator (the desired format).

General Recommendations for Prompt Design:

1. Start Simple and Iterate: Prompt design is an iterative process that begins with simple prompts, adding complexity and context incrementally to achieve optimal results.
2. Be Specific: The more detailed and specific your instruction is, the better the results will be. For example, instead of asking for a short explanation, specify the length and audience (e.g., "Use 2-3 sentences to explain the concept of prompt engineering to a high school student").
3. Use Instructions Clearly: Use commanding keywords like "Write," "Classify," "Summarize," or "Translate". Instructions are often best placed at the start of the prompt, separated from the context using a clear delimiter like "###".
4. Focus on Doing, not Not Doing: Avoid instructing the model on what not to do; instead, specify precisely what it must do. This leads to greater focus and better results.

Applications and Techniques Covered

Prompt engineering enables LLMs to perform various complex tasks. We showcase examples for:
• Text Summarization: Clearly instructing the model on the desired output length or format (e.g., "Explain the above in one sentence").
• Information Extraction: Using prompts to pull specific data points (like model names or places) from text.
• Question Answering (Q&A): Employing structured prompts with context and specific output constraints (e.g., "Keep the answer short and concise. Respond 'Unsure about answer' if not sure about the answer").
• Text Classification: Utilizing examples (Few-shot prompting) to guide the model toward a specific output format, ensuring consistency in labels (e.g., returning 'neutral' instead of 'Neutral').
• Dialogue/Role Prompting: Instructing the LLM system on its personality and intended behavior (e.g., setting a technical/scientific tone or giving easily understandable answers).
• Code Generation: Generating code snippets or complex database queries (like MySQL) by providing schema information and instructions.
• Reasoning: Improving complex problem-solving (like arithmetic) by instructing the model to break the problem into steps, leading to much better accuracy.