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3 posts tagged with "prompt engineering"

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8 Advanced Prompt Parsing Methods for AI Models

· 4 min read

For parsing the IPrompt_v3_for_promptsFactory[] data structure, we can consider the following approaches to transform it into precise prompts for large language models:

  1. Template-Based Concatenation
  2. Structured Assembly Method
  3. Priority-Based Sorting
  4. Conditional Dynamic Assembly
  5. Tag-Based Filtering Enhancement
  6. History-Aware Recursive Parsing
  7. Model-Specific Optimization
  8. Interactive Construction Method

Google AI Prompt Engineering Best Practices: 12 Key Techniques from 2025 White Pape

· 7 min read
xiaoka
xiaoka
Hi Here is xiaoka that likes to study efficiency tools and thinking patterns~

1. Provide examples​

One-shot/few-shot: You can provide a single example or multiple examples.

Providing examples allows the model to analyze and capture the characteristics of the results that are similar to the examples, achieving better outcomes.

Example 1: Product description generation (Few-shot)​

Prompt:

Write an e-commerce description for a new Bluetooth headset based on the example:

Example product: Smartwatch
Features: 50m water resistance/2-week battery life/blood oxygen detection
Copy: Challenge the depths of 50 meters, accompanying you to explore the underwater world. Long-lasting battery life of 14 days, continuous health monitoring day and night...

New product: Wireless noise-canceling headphones
Features: 40dB active noise cancellation/30-hour battery life/spatial audio

Unlocking the Power of COSTAR Prompt Engineering: Practical Guide and Case Studies

· One min read

COSTAR Framework​

  • Context: Providing background information helps the Language Model (LLM) understand the specific scenario.
  • Objective: Clearly defining the task helps guide the focus of the Language Model.
  • Style: Specifying the desired writing style makes the response from the Language Model more consistent.
  • Tone: Setting the tone ensures the response matches the required emotional expression.
  • Audience: Identifying the target audience makes the response from the Language Model more targeted.
  • Response: Providing the response format, such as text or JSON, ensures the output of the Language Model and assists in building workflows.

Resources​

https://medium.com/@frugalzentennial/unlocking-the-power-of-costar-prompt-engineering-a-guide-and-example-on-converting-goals-into-dc5751ce9875

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