AI Evaluation Platforms: When to Buy vs Build

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-AI-evaluation-platforms-creators

1. A company has a small engineering team of 4 people and a straightforward customer support chatbot. They need to evaluate AI responses for accuracy. Based on the framework discussed, which approach would likely be most appropriate?

   1. Outsource all AI evaluation to a third-party consulting firm
   2. Build a custom lightweight evaluation system tailored to their specific chatbot
   3. Skip formal evaluation and rely on user feedback alone
   4. Purchase an enterprise-grade platform like Weighth & Biases designed for large teams

2. What does an AI evaluation platform's 'coverage' refer to?

   1. The physical data centers where the platform stores evaluation data
   2. The range of evaluation methods supported, such as offline testing, live monitoring, and regression checks
   3. The number of employees who can access the platform simultaneously
   4. The marketing reach of the platform vendor

3. Which of the following is identified as something AI evaluation platforms CANNOT do, regardless of how sophisticated the platform is?

   1. Generate revenue for the company without human intervention
   2. Eliminate the need for any human oversight of AI outputs
   3. Automatically fix hallucinations in production AI systems
   4. Replace the need for organizational discipline in evaluation design

4. A company is choosing between Braintrust, LangSmith, and building a custom solution. They plan to eventually switch AI providers as the technology evolves. What should they prioritize in their evaluation?

   1. Choosing the cheapest platform available
   2. Migration ease and avoiding vendor lock-in
   3. Going with whatever their competitors use
   4. Selecting the platform with the most complex features

5. What is integration cost in the context of AI evaluation platforms?

   1. The fees paid to cloud providers hosting the platform
   2. The purchase price of the software license
   3. The technical effort and resources required to connect the platform with existing systems and workflows
   4. The cost of hiring additional engineers to run the platform

6. A large enterprise with 200+ engineers deploying multiple AI products across different domains is most likely to benefit from:

   1. Building their own evaluation system from scratch
   2. Avoiding AI evaluation entirely to reduce costs
   3. Buying a comprehensive evaluation platform with extensive integrations
   4. Using only manual human review for all AI outputs

7. What does the lesson identify as a key input for the buy-vs-build decision framework?

   1. Existing infrastructure and technology stack
   2. Employee satisfaction scores
   3. Company's social media following
   4. Office real estate locations

8. When planning platform adoption, which question is most important to answer first?

   1. What is the CEO's personal preference for tools?
   2. How much does the platform cost per user?
   3. Which platform has the best TV commercials?
   4. Who will use the platform and at what stages of development?

9. What is regression testing in the context of AI evaluation?

   1. Checking that new AI changes don't break previously working functionality
   2. Measuring how quickly an AI system learns from new data
   3. Comparing AI performance against human employee performance
   4. Testing how an AI system handles data from different geographic regions

10. A company chooses to build their own evaluation system instead of buying a platform. What burden do they likely still face regardless of their choice?

    1. Ongoing maintenance and updates to keep the system effective
    2. Finding investors to fund the evaluation project
    3. Hiring additional data scientists for unrelated projects
    4. Purchasing expensive GPU hardware for evaluation

11. What type of evaluation does 'online monitoring' refer to?

    1. Testing AI performance on historical datasets
    2. Comparing different AI models in a sandbox environment
    3. Evaluating AI systems before they are deployed
    4. Assessing AI behavior in real-time during production use

12. A company evaluates three platforms and finds Platform A covers 90% of their needs, Platform B covers 70%, and Platform C covers 85%. Platform A costs twice as much as the others. What should guide the final decision?

    1. Choosing the cheapest platform to minimize budget
    2. Picking the platform with the lowest coverage to save money
    3. Weighing whether the additional 5-20% coverage justifies the higher cost
    4. Selecting whichever platform has the highest coverage regardless of cost

13. The lesson mentions which of the following as an example of an AI evaluation platform?

    1. Slack
    2. Braintrust
    3. Salesforce
    4. Zoom

14. What does 'offline evaluation' mean in AI evaluation terminology?

    1. Assessing AI performance in countries with limited connectivity
    2. Testing AI systems on historical data without affecting live systems
    3. Running AI tests during non-business hours
    4. Evaluating AI only on computers without internet connections

15. When the lesson warns about 'platform lock-in,' what specific risk is being highlighted?

    1. Difficulty switching to different evaluation tools or platforms in the future
    2. Being unable to use AI systems without the platform
    3. Getting sued by the platform vendor
    4. Losing access to the platform if internet connectivity fails