Instruction Following Prompts

While basic prompts can elicit general responses from Large Language Models (LLMs), building reliable applications often requires more precise control. Simply asking an LLM to "write about topic X" might yield varied results in terms of length, focus, and format. Instruction following prompts address this by providing explicit, detailed directives to the model about the task it needs to perform. Think of it less like asking a question and more like giving a command or a set of specifications.

Effective instruction following hinges on clarity and specificity. The goal is to leave as little ambiguity as possible regarding what you expect the LLM to do. Unlike few-shot prompting, which relies heavily on examples, instruction following focuses on the command itself.

Key Components of Instruction Following Prompts

Well-crafted instructions typically include several components:

1. Clear Action Verb: Start with a direct verb that defines the primary task. Examples include "Summarize," "Translate," "Extract," "Generate," "Rewrite," "Classify," "Compare," "Explain."
2. Subject/Input Specification: Clearly state what the LLM should operate on. This could be text provided directly in the prompt, a reference to a type of input, or context given earlier.
3. Constraints and Requirements: Define the boundaries and rules for the task. This is where you add precision. Examples:
   • Length constraints: "in under 100 words," "as a single paragraph," "in exactly three bullet points."
   • Content constraints: "focusing on the technical aspects," "mentioning the advantages and disadvantages," "excluding any mention of pricing."
   • Style/Tone constraints: "in a formal tone," "using simple language suitable for a 10th grader," "written from the perspective of an expert."
4. Output Format Specification: Explicitly state how the output should be structured. Examples: "Provide the output as a JSON object with keys 'name' and 'summary'," "Format the results as a Markdown table," "List the items separated by commas."
5. Negative Constraints (Optional): Sometimes it's helpful to specify what the LLM should not do. Example: "Do not include your own opinions," "Avoid using overly technical terms," "Do not add any introductory or concluding remarks."

Examples: From Vague to Specific

Let's see how adding clear instructions improves prompts:

Example 1: Summarization

• Vague Prompt: Summarize this text: [Long article text]
• Instruction Following Prompt:

  Summarize the following text in exactly two sentences, focusing on the main conclusion presented by the author. Do not include examples mentioned in the text.
  
  Text: [Long article text]
  

Example 2: Information Extraction

• Vague Prompt: Find the important stuff in this email: [Email text]
• Instruction Following Prompt:

  Extract the sender's name, the meeting date, and the meeting time from the following email text. Format the output as a JSON object with the keys "sender_name", "meeting_date", and "meeting_time". If any piece of information is missing, use null for its value.
  
  Email Text: [Email text]
  

Example 3: Code Generation

• Vague Prompt: Write Python code for reading a file.
• Instruction Following Prompt:

  Generate a Python function called `read_text_file` that takes one argument: `file_path` (a string).
  The function should:
  1. Open the file specified by `file_path` in read mode.
  2. Read the entire content of the file.
  3. Handle potential `FileNotFoundError` exceptions by returning None if the file does not exist.
  4. Return the content of the file as a single string if successful.
  Include a docstring explaining what the function does, its arguments, and what it returns. Do not include any example usage code outside the function definition.
  

Tips for Effective Instruction Design

• Be Unambiguous: Read your instructions as if you know nothing about the task. Could they be misinterpreted?
• Use Action-Oriented Language: Start instructions with strong verbs.
• Structure Complex Instructions: For tasks with multiple steps or constraints, use bullet points or numbered lists within your prompt to clearly separate instructions.
• Prioritize Clarity: Use simple, direct language. Avoid jargon unless the task specifically requires it (and the LLM is expected to understand it).
• Iterate and Refine: Your first attempt at instructions might not be perfect. Test the prompt, observe the output, and refine the instructions based on the results. This iterative process is fundamental to prompt engineering (as we'll discuss further in Chapter 3).

When to Employ Instruction Following

Instruction following is particularly useful when:

• The task is complex or has multiple requirements.
• A specific output format is necessary for downstream processing in an application.
• You need to control the tone, style, or persona of the response more precisely than role prompting alone allows.
• You need to guide the LLM through a specific sequence of steps (though Chain-of-Thought prompting, covered later, specializes in reasoning steps).

While zero-shot and few-shot prompts are effective for simpler tasks or when demonstrating a pattern is sufficient, instruction following provides a more direct and controllable mechanism for guiding LLM behavior in sophisticated applications. It forms a core part of the prompt engineer's toolkit for achieving reliable and predictable outcomes.