Improve Prompt Engineering for All Content Tools

[nafisfaysal]

Background:

Our current content management platform consists of various tools designed for different content generation needs, including Amazon listings, blogs, business content, cooking recipes, CVs, demo content, emails, social media posts, features, Fiverr gigs, Google content, headlines, Instagram posts, LinkedIn content, product descriptions, sales content, website content, writing, and YouTube content. Each tool utilizes specific prompt engineering to generate relevant content. To enhance the quality and relevance of the AI-generated content, we need to improve the prompt engineering across all these tools.

User Story:

As a backend developer, I want to improve the prompt engineering for all content generation tools so that the generated content is more accurate, relevant, and aligned with the user's needs.

Acceptance Criteria:

1. Analyze Existing Prompts:

   • Review the current prompt engineering logic for each content tool listed:
     • Amazon (amazon.contents.js)
     • Blog (blog.contents.js)
     • Business (business.contents.js)
     • Common (common.contents.js)
     • Cooking (cooking.contents.js)
     • CV (cv.contents.js)
     • Demo (demo.contents.js)
     • Email (email.contents.js)
     • Extension (extension.contents.js)
     • Facebook (facebook.contents.js)
     • Features (features.contents.js)
     • Fiverr (fiverr.contents.js)
     • Google (google.contents.js)
     • Headline (headline.contents.js)
     • Index (index.js)
     • Instagram (instagram.contents.js)
     • LinkedIn (linkedIn.contents.js)
     • Product (product.contents.js)
     • Sales (sales.contents.js)
     • Website (website.contents.js)
     • Writing (writing.contents.js)
     • YouTube (youtube.contents.js)

2. Develop Improved Prompts:

   • Enhance the prompt engineering for each tool to ensure better context understanding and relevance.
   • Tailor prompts to include specific keywords, phrases, and structures that align with the expected output for each content type.

3. Integration and Testing:

   • Implement the improved prompts into their respective JavaScript files.
   • Conduct thorough testing to ensure the new prompts generate content that meets quality standards.
   • Validate that the updated prompts do not introduce any errors or unexpected behavior in the content generation process.

4. User Feedback Mechanism:

   • Implement a feedback mechanism for users to rate the quality of the generated content.
   • Collect and analyze this feedback to further refine and improve prompt engineering.

5. Documentation:

   • Update the internal documentation to reflect changes in the prompt engineering logic.
   • Provide guidelines and examples of the new prompts for future reference and maintenance.

Tasks:

1. Analysis Phase:

   • Review the current prompt logic in each JavaScript file.
   • Identify areas of improvement for better content relevance and accuracy.

2. Development Phase:

   • Create improved prompts for each content tool.
   • Integrate the new prompts into the respective JavaScript files.

3. Testing Phase:

   • Conduct unit and integration tests to ensure the new prompts work correctly.
   • Validate content quality and relevance through user testing.

4. Feedback Collection:

   • Implement user feedback mechanisms.
   • Collect and analyze feedback to make further adjustments as needed.

5. Documentation Update:

   • Update documentation to include new prompt structures and guidelines.

By improving the prompt engineering for all content tools, we aim to enhance the overall user experience, ensuring that the AI-generated content is more accurate, relevant, and aligned with the specific needs of our users.