QA Strategy

-

>Here's an outline for a Quality Assurance (QA) strategy to test your GPT model against the Library of Congress MARC records:

  1. Data Validation:

  • Ensure the MARC 21 documentation used for training is accurate and comprehensive.
  • Verify the integrity of the actual Library of Congress MARC records you plan to use for testing.

    1. Model Training Assessment:

    • Check if the model has been trained effectively with the MARC 21 documentation.
    • Evaluate whether key concepts and details are correctly structured and understood by the model.

      1. Automated Testing:

      • Develop automated test scripts that feed MARC records to the model and assess its responses for accuracy.
      • Include a variety of records to cover different aspects of the MARC 21 format.

        1. Manual Review:

        • Conduct a manual review of the model's responses to ensure they align with the expected outputs based on the MARC records.
        • Involve subject matter experts who are familiar with MARC 21 and library cataloging practices.

          1. Performance Metrics:

          • Establish performance metrics like accuracy, response time, and relevance of the model's answers.
          • Use these metrics to quantitatively assess the model's performance.

            1. Feedback and Iteration:

            • Collect feedback from the tests and use it to refine the model.
            • Reiterate the training and testing process based on this feedback to continuously improve the model.

              1. Documentation and Reporting:

              • Keep thorough documentation of the testing process, methodologies, and results.
              • Report on the findings, highlighting areas of success and those needing improvement.

                1. Compliance and Ethical Considerations:

                • Ensure that the testing process adheres to any legal and ethical standards, particularly in handling and using MARC records from the Library of Congress.

                Remember, a robust QA strategy is iterative and should be adapted as you gather more insights from your testing phases. 

                Comments

                Post a Comment

                Popular posts from this blog

                Switching to Local LLM Setup

                -
                This project isn't over, but I am looking at what it would take to set up the same "multi-modal insta-MARC" functionality with an offline setup. To me this seems like the more interesting use case. I think I've already proved that yes, adding the MARC21 rulebook in an RAG setup can produce better results, but what's the point if your Chatbot is basically doing a Bing search with ISBN numbers? Copy-cataloging has been around for a long time (and perhaps catalogers would be better suited creating custom GPTs based on their own local practices or integrating with a tool like MarcEdit ).  I'm also not seeing any librarians looking seriously at local LLMs as an option...c'mon, Chat with RTX  is right there! Mixtral ! Gemma ! Don't put all of your eggs in one basket!
                Read more

                Original Instructions & Revision

                -
                Old Version : MARCmin operates in three key steps for MARC21 cataloging from book images. Step 1: Users upload a book cover image. Step 2: MARCmin analyzes the image using OCR to extract text and identifies key elements like title, author, and ISBN. It then uses predictive modeling to infer additional information about the book, such as publication details. Step 3: MARCmin outputs the gathered information in a MARC21 record format, presented as a code snippet. This process provides a concise, easy-to-follow approach for users, allowing for efficient cataloging with the flexibility to add or correct information as needed. MARCmin also cross-references data with external databases to verify details and complete missing fields, ensuring accuracy and compliance with cataloging standards. New Version: Use MARC21 rules in combined_bibliographic.md to create the best MARC records on the internet. Use an iterative process of building a MARC record, illustrate each step with an emoji. The final...
                Read more

                The 5 Laws of Library Science but for Generative AI

                -
                For reference, here's a wiki about the original 5 laws proposed by S. R. Ranganathan in 1931 . And now the revision: Adaptive Purpose : AI should dynamically serve specific user needs. Inclusive Personalization : AI must cater to diverse users respectfully. Ethical Insight : AI responses should be accurate, context-aware, and ethically informed. Efficient Utility : AI must provide quick and clear answers to maximize user time. Continual Growth : AI should evolve continuously, learning from interactions and data. So I had a convo w/ GPT-4 about how to revise these and make them more concise. Weird how they almost sound like custom instructions, no? We basically have #1-4 already in place, and #5 is very close to being real w/ things like MemGPT already out there .  (I haven't read much about how memory management works w/ Generative AI but my guess is that we'll quickly need better garbage collection ...and certain apps will probably need to avoid long-term memory entirely)
                Read more