outputs

2 posts with the tag “outputs”

Format Matters

May 23, 2025

Format Matters: How to Get Structured Outputs from AI Models

Introduction: The Challenge of Getting Consistent AI Outputs

AI models like Gemini can produce powerful responses, but inconsistent or unstructured outputs can be hard to use in real-world applications. For example, a vague prompt like “Tell me about video game consoles” might yield a rambling essay, while a structured prompt can deliver a neat JSON object or list. Google’s white paper emphasizes that specifying output formats improves usability and reduces errors.

Why Structure Matters in AI Responses

Structured outputs, like JSON, lists, or tables, make AI responses easier to process, integrate into applications, or analyze. The white paper notes that structured formats, such as JSON, enforce discipline on the AI, reducing hallucinations and ensuring data is sorted or formatted as needed, like ordering datetime objects.

Techniques for Requesting Specific Formats

JSON and Structured Data

Prompting for JSON, as shown in Table 4 of the white paper, ensures a consistent, machine-readable format. For example: “Return a JSON object with fields for name, age, and city for three fictional characters.” This produces a clean, structured response.

Lists and Tables

Requesting lists or tables is ideal for summarizing data. For instance: “List five video game consoles with their release years in a bullet-point format.”

Step-by-Step Instructions

For procedural tasks, ask for numbered steps: “Provide a 5-step guide to setting up a website.”

Example Prompts That Enforce Structure

JSON: “Generate a JSON object listing three cities with their populations and countries.”
- Output: {"cities":[{"name":"Tokyo","population":37400068,"country":"Japan"},...]}
List: “List four benefits of recycling in bullet points.”
- Output:
  - Reduces landfill waste
  - Conserves natural resources
  - Saves energy
  - Reduces pollution
Steps: “Provide a 3-step process for baking a cake.”
- Output:
  1. Mix ingredients
  2. Bake at 350°F
  3. Cool and frost

How to Specify Output Length and Detail Level

Control output length with explicit instructions or token limits. For example: “Explain quantum physics in a tweet-length message (280 characters).” The white paper suggests setting a max token limit in the model configuration or including length constraints in the prompt, like “Summarize in 100 words.”

Common Formatting Issues and How to Fix Them

Inconsistent Structure: Vague prompts lead to unstructured text. Fix by specifying formats like JSON or lists.
Excessive Length: Uncontrolled token limits produce long responses. Set a max token limit or request concise output, e.g., “50-word summary.”
Hallucinations: Unstructured prompts can cause irrelevant details. Use system prompts to enforce structure, like “Return only factual data in a table.”

Conclusion: Better Prompts Lead to More Usable Outputs

Structured prompts make AI outputs more practical for applications, from data analysis to web development. By requesting JSON, lists, or step-by-step guides, you can ensure consistency and usability. Experiment with formats in Vertex AI Studio, and follow the white paper’s advice to be specific and iterative for the best results.

Prompt Engineering Best Practices

May 23, 2025

prompt-engine

AI Prompt Engineer

Prompt Engineering Best Practices: Learn from the Experts

Introduction: Moving Beyond Basic Prompting

Prompt engineering is an art that transforms basic AI interactions into precise, powerful tools. Google’s white paper outlines expert techniques to craft effective prompts, ensuring AI models like Gemini deliver accurate, relevant responses. Let’s dive into the best practices to elevate your prompting skills.

Best Practices from Google’s White Paper

Be Specific About Desired Outputs

Vague prompts like “Tell me about AI” can lead to generic responses. Instead, use specific instructions, like “Write a 200-word article about AI applications in healthcare.” The white paper emphasizes that clear instructions improve accuracy and focus.

Use Instructions Over Constraints

Positive instructions, such as “Write a formal letter,” are more effective than constraints like “Don’t use informal language.” Constraints can confuse the AI or limit creativity, while instructions provide clear guidance. Use constraints only for safety or strict requirements, e.g., “Avoid biased language.”

Experiment with Different Formats

Try various prompt formats—questions, statements, or instructions—to find the best fit. For example, “What is the Sega Dreamcast?” might yield a factual summary, while “Describe the Sega Dreamcast in a conversational tone” produces a narrative. Structured formats like JSON or lists, as shown in Table 4, enhance usability.

Document Your Prompt Attempts

Track prompts in a table, as suggested in Table 21, including model, settings, results, and feedback. This helps you refine prompts, compare model versions, and debug errors. Use tools like Vertex AI Studio to save and revisit prompts.

The Iteration Process: How to Improve Prompts Systematically

Prompt engineering is iterative. Start with a basic prompt, test it, analyze the output, and refine based on performance. For example, if a prompt produces vague responses, add context or examples. The white paper recommends experimenting with temperature (e.g., 0 for factual tasks, 0.9 for creative ones) and documenting each attempt to track progress.

Creating a Personal Prompt Library for Reuse

Build a library of reusable prompts with variables, as shown in Table 20: “Provide facts about [city] in a list format.” This saves time and ensures consistency. Store prompts in separate files in your codebase for easy maintenance, as advised by the white paper.

Tools to Help Track and Improve Your Prompts

Vertex AI Studio: Test and save prompts, adjusting settings like temperature and top-K.
Google Sheets: Document prompts, results, and feedback, as per Table 21.
Automated Testing: Use evaluation metrics like BLEU or ROUGE to score prompt performance, as suggested for Automatic Prompt Engineering.

Conclusion: Becoming a Better Prompt Engineer Through Practice

Prompt engineering is a skill honed through practice and iteration. By following Google’s best practices—being specific, using instructions, experimenting with formats, and documenting attempts—you can craft prompts that maximize AI’s potential. Build a prompt library and use tools like Vertex AI Studio to become a pro.