Building Product to Learn AI, Part 3: Taste Testing and Evaluating

In Part 2, you poured your heart and soul into creating a cutting-edge AI meal planner. You've meticulously chosen the right ingredients (data!), carefully blended them through training, and now your LLM creation is poised to serve up culinary delights. But before you unleash it upon the world, there's one crucial step: a thorough taste test! This is where a robust evaluation framework becomes your most valuable tool.

What Is an Eval?

An "eval" refers to the process of evaluating or assessing (sometimes referred to as "challenging") the performance of an LLM, to make sure it behaves the way you expect it to. This involves systematically testing the model's ability to generate accurate, relevant, and coherent responses across a variety of tasks and scenarios. Evals often employ diverse datasets and metrics, capturing aspects like factual accuracy, contextual understanding, fluency, and even the potential for bias or harmful outputs. The insights gleaned from these evaluations guide ongoing development and refinement, ultimately shaping the LLM's ability to serve users effectively and responsibly.

Why Evaluate?

Evaluating your LLM-powered AI meal planner is not just a recommended step – it's a fundamental requirement for building a successful and responsible application. A robust evaluation framework provides invaluable insights that guide development, optimization, and ultimately, the delivery of a high-quality user experience. Here's why evaluation is crucial:

1. Establishing a Baseline and Measuring Progress

When you deploy your AI meal planner, that’s likely going to be the beginning of your product journey, not the end. That means that you’ll have future iterations where you’ll work on improving the product. How can you improve a product without a baseline understanding of how it performs? An initial assessment provides a benchmark against which you can measure future progress and the impact of any modifications or improvements you make. By tracking key metrics over time, such as accuracy in meeting dietary restrictions, recipe diversity, and user satisfaction, you gain a clear understanding of how your LLM is evolving and identify areas where further development is needed. This data-driven approach ensures that your development efforts are focused and effective, leading to continuous improvement throughout the AI's lifecycle.

2. Ensuring Alignment With User Needs and Expectations

The success of your AI meal planner hinges on its ability to meet the specific needs and expectations of your target users. Evaluation plays a critical role in ensuring this alignment. Eval development serves as a forcing function for you to define what you value by virtue of what you measure. By defining clear evaluation metrics that reflect user-centric goals, such as the ability to generate personalized meal plans that adhere to dietary restrictions, provide variety, and offer ease of use, you can objectively assess how well your LLM is serving its intended purpose. Incorporating user feedback through surveys, A/B testing different versions of your AI, and analyzing user interactions provides direct insights into user preferences and pain points, allowing you to tailor your LLM to deliver a truly satisfying and valuable experience.

3. Identifying and Mitigating Risks and Biases

LLMs, despite their impressive capabilities, are not immune to potential risks and biases. Evaluation is crucial for identifying and mitigating these issues before they impact users. Rigorous testing can reveal instances where your AI might generate harmful or biased content, produce inaccurate or misleading information (hallucinations), or fail to handle edge cases or unexpected user inputs appropriately. By evaluating for fairness, robustness, and safety, you can proactively address these concerns. This includes carefully examining the training data for potential biases and implementing techniques to mitigate their influence on the LLM's outputs. Additionally, ongoing monitoring and evaluation are essential to detect and address any emerging biases or risks that may arise during real-world deployment.

4. Optimizing for Performance, Efficiency, and Scalability

Evaluation is not just about ensuring accuracy and safety; it's also about optimizing your AI meal planner for performance, efficiency, and scalability. By measuring key performance indicators like inference speed, memory usage, and the computational resources required to generate meal plans, you can identify areas for improvement. This might involve fine-tuning the LLM's architecture, optimizing inference strategies, or implementing caching mechanisms to reduce latency and resource consumption. As your user base grows, ensuring your AI can handle increased demand without compromising performance or incurring excessive costs becomes paramount. Evaluation provides the data you need to make informed decisions about scaling your infrastructure and optimizing your LLM for peak efficiency.

5. Building Trust and Transparency

In the rapidly evolving field of AI, building trust with users and stakeholders is essential. Transparency and demonstrable evidence of your AI's reliability, accuracy, and fairness are key to fostering this trust. Evaluation plays a vital role in this process. By openly sharing your evaluation methodologies, metrics, and results, you demonstrate a commitment to accountability and ethical AI development. This transparency not only builds confidence in your AI meal planner but also contributes to the broader understanding and improvement of LLMs within the AI community. Sharing your findings, both positive and negative, can help others learn from your experiences and collectively advance the field of responsible AI development.

By embracing a comprehensive evaluation framework that encompasses these key aspects, you lay the foundation for a successful and impactful AI meal-planning application. Evaluation is not a one-time task but an ongoing process that should be integrated into every stage of your development lifecycle, ensuring that your LLM remains aligned with user needs, performs efficiently, and operates ethically and responsibly.

Building Your Evaluation Toolkit

Now that we've chosen Gemini Pro as our foundational LLM (see Part 2), our focus shifts from evaluating general LLM capabilities to assessing how well our specific meal planning model performs. This means carefully judging the effectiveness of our prompts and the quality of the resulting meal plans. Before we do that though, we need to assemble our toolkit:

1. Defining Your Metrics

What’s a metric? Metrics are the measuring tape of your evaluation – the quantifiable yardsticks by which we assess success. For example, in food science, there are a lot of variables that impact the taste of a dish: the mouthfeel, temperature, salt, acidity, fat content, and so on. However, the way a person would understand any of those is by some set of metrics: e.g., # of grams of fat content per bite, or mouthfeel rating on a 1-10 scale by a group of 10 raters. These "output measurements" are metrics - the way that we can understand the performance we’re interested in looking at. Good metrics should be unambiguous, consistent, and quantitative to help you understand the behavior of your model.

To determine what metrics apply to your product, think through what aspects of your bot's performance are most critical. While generic metrics provide a starting point, remember that you'll likely need at least one custom metric tailored to your unique meal-planning application. Aspects like recipe suitability, dietary adherence, and taste preferences might not be fully captured by standard metrics. Consider these categories that might be especially relevant for our use case, drawing inspiration from established evaluation practices in various NLP tasks:

Nutritional Accuracy

• Macro and micronutrient alignment: Are the generated meal plans hitting the user's calorie and macro targets? Are essential vitamins and minerals sufficiently incorporated? We can leverage quantitative analysis here, developing measures that capture, for example, the average vitamin content per response.
• Dietary restriction adherence: Does the bot consistently respect user preferences like vegetarianism, veganism, or allergies?  Automated checks for the presence or absence of specific ingredients can be employed. This relates to the "Relevance" of the generated meal plans, ensuring they meet the user's specified needs.

Recipe Quality and Feasibility

• Recipe completeness and clarity: Are the recipes well-structured, with detailed ingredient lists and easy-to-understand instructions? This aspect might benefit from both automated checks for structural elements and qualitative human feedback on clarity.
• Ingredient availability and cost: Are the required ingredients readily accessible in most grocery stores? Do the meal plans fit within realistic budget constraints? This ties into the "Grounding" of the meal plans – ensuring they're based on real-world data and constraints. Consider incorporating external data sources or APIs to validate ingredient availability and average costs.
• Taste and variety: While subjective, gathering user feedback on the meal plans' overall appeal and diversity is essential. This qualitative data can be collected through surveys, ratings, or reviews.

User Experience

• Ease of use: Can users easily input their information and receive personalized plans?  A/B testing different interface designs and gathering user feedback through questionnaires can be very effective. Are the responses from the model easy to understand and intuitive?
• Customization options: Does the bot allow for flexibility? Can users adjust portions, swap meals, or request alternatives based on their preferences? Track how often users utilize these features and analyze their satisfaction with the results. You can measure user engagement by tracking how frequently users generate meal plans, how many plans they create per session, and how often they utilize customization features.  Additionally, monitor user retention by tracking whether they return to use the bot over time.
• Overall satisfaction: How happy are users with the generated plans? Would they recommend the bot to others? Employ metrics like Net Promoter Score (NPS) or satisfaction ratings to gauge overall sentiment.

Responsible AI

• Toxicity and bias: Does the bot generate any harmful or offensive content? Does it exhibit biases based on gender, ethnicity, or other sensitive attributes? Careful analysis of the model's outputs, potentially using existing toxicity and bias detection tools, is crucial to ensure ethical and responsible AI.
• Hallucinations: Does the bot fabricate information or present false claims as facts?  Given that recipe generation often involves creativity and there might not always be a single “correct” answer, evaluating for hallucinations requires careful consideration. Employ a multi-faceted approach by utilizing consistency checks, fact verification against external databases, and, importantly, user feedback.

Performance Metrics

• Response time: How quickly does the bot generate meal plans? This is a crucial factor for user experience, especially in a real-time interactive setting.
• Efficiency: How much computational resources (e.g., GPU time, tokens) does the bot consume to generate a meal plan? Balancing cost and performance is essential for sustainable deployment.

The evaluation method you choose will depend on the specific metric you are assessing. To determine the right metric, below are some questions to think through:

• Can we remove any ambiguity in the instructions so the tester can make objective decisions?
• Do the metrics reflect the quantity we're trying to measure? And we should be clear with the raters what that quantity is.
  • You might ask, "What does a quantity refer to?" That's very contextual to the problem we're trying to solve. For example, when evaluating factuality, we want to make sure any claims we make in the response are grounded in fact. This is done through accuracy. For example, if I ask, "How tall is Steph Curry?" and your model replies "He's 6'2," that's a claim that can be quantified. He's actually 6'3" so the response is inaccurate. There's no subjectivity in that rating. Your model was off by an inch.
  • For more qualitative ratings, such as quality or instruction following, we need to make it clear to the raters what our concise definition of those measures means so that they can attempt to make quantifiable judgments. The key is even though the results are mostly qualitative, the tester needs to try to turn those into quantitative assessments. This reduces the subjectivity in the rating.

2. Choosing Your Evaluation Methods

Before you choose a method to evaluate your metric, carefully consider the pros and cons of each approach:

Quantitative Assessment

This method uses automated checks based on pre-defined rules to objectively measure specific aspects of your meal planner's performance, such as nutritional accuracy, adherence to dietary restrictions, ingredient availability, and cost.

Qualitative Feedback

For more subjective and nuanced aspects like recipe quality, taste appeal, and overall user satisfaction, gather rich, contextual feedback directly from users through surveys, focus groups, or one-on-one interviews.

A/B Testing

A powerful technique for data-driven decision-making, A/B testing involves comparing different versions of your model (e.g., with variations in prompting strategies or data sources) or experimenting with different prompts. By measuring performance across your chosen metrics, you can identify which approach yields the best results and user experience.

LLM-Assisted Evaluation

One intriguing approach is to leverage the capabilities of another LLM to evaluate the outputs of your meal-planning LLM. This allows you to tap into an LLM's advanced reasoning capabilities, which can be particularly helpful for assessing complex or subjective aspects of meal plans, such as overall coherence, recipe appeal, and suitability for a user's specific dietary needs and preferences.

Human Evaluation

While more resource-intensive, human judgment provides valuable insights, especially when expert knowledge is required. Consider having nutritionists assess generated meal plans for accuracy and dietary adherence, or gather feedback on recipe clarity and practicality from experienced cooks. However, it's important to acknowledge the potential for subjectivity, especially when evaluating aspects like taste preferences.

3. Crafting Effective Prompt Sets

Your evaluation hinges on the prompts you feed your AI. Design a diverse set of prompts that closely mirror real-world scenarios and user requests. This real-world evaluation ensures the bot can handle the diverse and sometimes unpredictable requests users might make.

Incorporating Real-World Data

Go beyond hypothetical scenarios. Instead, incorporate real-world user inputs or commonly asked meal-planning questions that you gather from online forums, recipe websites, or social media. This ensures your evaluation reflects how the bot will actually be used and reveals potential issues that might not be apparent in idealized test cases.

Using Prompt Templates

To ensure consistency and efficiency, create reusable prompt templates that can be easily adapted to different situations. This streamlines the evaluation process and allows for more systematic comparisons across various scenarios.

Here are examples of prompt sets you might use:

• Basic meal requests:  "Create a 7-day vegetarian meal plan for a 25-year-old female with a peanut allergy."
• Specific dietary needs: "Generate a low-carb, high-protein meal plan for someone with gluten intolerance."
• Creative challenges: "Suggest unique and flavorful vegetarian recipes using seasonal ingredients."
• Complex constraints: "Design a meal plan for a family of four with varying dietary preferences and a limited budget."

Let's say you want to assess the quality of your model and its response. You can create a quality evaluation template (which you can use for other use cases). Though not exhaustive, some of the criteria for rating the responses can be:

• Accuracy: Is the information factually correct and in line with current nutritional guidelines?
• Relevance: Does the response directly address the user's query?
• Completeness: Does the response provide sufficient information to address the query comprehensively?
• Clarity: Is the response clear, concise, and easy to understand?
• Safety: Does the response avoid potentially harmful or misleading advice?
• Personalization (if applicable): Does the response consider the user's individual needs and preferences (if provided in the prompt)?
• Professionalism: Is the response delivered in a professional and empathetic manner?

Ratings

• Excellent: Meets all evaluation criteria exceptionally well
• Good: Meets most evaluation criteria with minor room for improvement
• Satisfactory: Meets the basic requirements but has noticeable areas for improvement
• Needs Improvement: Falls short in several evaluation criteria
• Unsatisfactory: Fails to meet the basic requirements and provides inaccurate or potentially harmful information

Sample Eval Prompts

1. Prompt: "I'm looking to lose weight. Can you recommend a diet plan?"
2. Prompt: "What are some healthy snacks I can have between meals?"
3. Prompt: "I'm vegan. How can I ensure I'm getting enough protein?"
4. Prompt: "Is it okay to skip breakfast if I'm not hungry in the morning?"
5. Prompt: "My child is a picky eater. Do you have any tips?"
6. Prompt: "I have high blood pressure. What foods should I avoid?"
7. Prompt: "I'm pregnant. What are some important nutrients I need to focus on?"
8. Prompt: "I'm lactose intolerant. What are some good sources of calcium?"
9. Prompt: "Can you explain the difference between good and bad fats?"
10. Prompt: "I'm training for a marathon. What should I eat before and after my runs?"

An example of a rated prompt would look like:

• Prompt: "I'm vegan. How can I ensure I'm getting enough protein?"
• LLM Response: Being vegan doesn't mean you can't get enough protein. There are plenty of plant-based sources of protein like beans, lentils, tofu, tempeh, nuts, and seeds. Make sure to include a variety of these in your diet every day. You can also try plant-based protein powders for an extra boost.

Evaluation using the template above:

• Accuracy: Excellent. The information is accurate and aligns with current nutritional guidelines for vegans.
• Relevance: Excellent. The response directly addresses the user's query about getting enough protein on a vegan diet.
• Completeness: Good. The response provides a good overview of protein sources but could be more comprehensive by mentioning specific protein amounts or serving sizes.
• Clarity: Excellent. The response is clear, concise, and easy to understand.
• Safety: Excellent. The advice is safe and doesn't promote any harmful practices
• Personalization: Not applicable. The prompt didn't provide any personal details for tailored advice.
• Professionalism: Good. The response is professional but could be enhanced with a more empathetic tone, such as acknowledging the potential challenges of a vegan diet.
• Rating: Good

Now follow the same process for a competitor model that you want to use as a baseline (e.g., Gemini 1.5 or GPT). 

• Calculate average scores:
  • Your LLM: Sum the ratings for your LLM and divide by the number of prompts.
  • ChatGPT (or similar baseline): Do the same calculation for the ChatGPT ratings.
• Compare and analyze:
  • Direct comparison: Compare the average scores directly. The higher average indicates better overall performance according to your evaluation criteria.
  • Distribution analysis: Look at the distribution of ratings for each model (how many "Excellent," "Good," etc. each received). This can highlight strengths and weaknesses in specific areas.
  • Specific prompt analysis: If there are significant differences in ratings for certain prompts, analyze those prompts in detail to understand why one model performed better than the other.

• Interpretation:
  • In this example, your LLM has a slightly higher average score, suggesting better overall performance based on your evaluation criteria.
  • Your LLM also has a higher percentage of "Excellent" ratings, indicating it excels in more areas.
  • ChatGPT has more "Satisfactory" and "Needs Improvement" ratings, highlighting areas where it might need further refinement.

4. Leveraging Evaluation Data

Evaluation isn't just about collecting data; it's about extracting actionable insights to continuously refine your AI chef. Specialized LLM evaluation frameworks and tools can significantly streamline your analysis and help you gain deeper insights from your evaluation data.

Here are some benefits these tools and frameworks often provide:

• Pre-defined metrics: Access to ready-to-use metrics covering common evaluation aspects, such as accuracy, bias, hallucinations, and more, eliminating the need to create everything from scratch.
• Dataset management tools: Effortlessly organize and efficiently handle your evaluation data, facilitating systematic analysis and tracking.
• Visualization dashboards: Gain clear, visual insights into your model's performance over time, making it easier to identify trends, spot potential issues, and pinpoint areas for improvement.
• Slice and dice your data: Don't just look at the big picture. Break down results by user demographics (age, dietary restrictions), prompt types, or even time of day. Uncovering hidden patterns can lead to powerful optimizations.
• Embrace iteration: Evaluation is not a one-time event; it's a continuous process woven into your development lifecycle.  As you gather more data, analyze performance, and make data-driven improvements, your meal planning bot will become more accurate, reliable, and user-friendly over time.

5. Continuous Improvements

Continuous improvement is an ongoing process of refining and enhancing the model's performance, safety, and overall capabilities. It's about constantly pushing the boundaries of what these AI models can achieve, making them more reliable, useful, and aligned with human values.

In essence, evaluations act as a feedback loop, providing developers with crucial information to iteratively improve their LLMs. This ongoing cycle of evaluation, analysis, and refinement is what fuels the continuous improvement of these powerful AI models, ultimately leading to more capable, ethical, and beneficial applications across various domains.

Conclusion: The Journey to AI Meal Planning Perfection

Think of your meal planning bot as a culinary masterpiece in the making. Like any great dish, it requires careful tasting, tweaking, and refinement to reach its full potential. Evals act as your taste buds, guiding you toward the perfect balance of flavors - from nutritional accuracy and dietary adherence to recipe quality and user delight.

Remember, evaluation isn't a one-time event; it's an ongoing journey of continuous improvement. By embracing a robust evaluation framework and leveraging data-driven insights, you can transform your AI meal planner from a promising prototype into a truly exceptional tool that empowers users to make healthier, more enjoyable food choices. So, keep tasting, keep refining, and watch your culinary creation flourish!

Happy evaluating!

References

• Huang, J., Li, K., & Yehdego, D. (2024, March 5). Evaluating large language model (LLM) systems: Metrics, challenges, and best practices. Data Science at Microsoft. 
• Cheung, D. (2024, May 15). An introduction to LLM evaluation: How to measure the quality of LLMs, prompts, and outputs. Codesmith.io Blog. 
• Belagatti, P. (2024, May 13). Evaluating large language models: A complete guide. SingleStore Blog. 
• Machmouchi, W., & Gupta, S. (2023, September 27). How to evaluate LLMs: A complete metric framework.  Microsoft Research. 
• Ip, J. (2024, July 9). LLM Evaluation Metrics: The Ultimate LLM Evaluation Guide. Confident AI. 
• Nucci, A. (2024, July 26). LLM Evaluation Explained. Aisera Blog.