## Overview Coursera is a global online learning platform serving millions of learners and enterprise customers. As they began integrating large language models into their product offerings, they recognized the critical need for a robust evaluation framework to ensure their AI features met quality standards before reaching production. This case study, published in May 2025, details how Coursera built a structured evaluation process to ship reliable AI features, specifically focusing on their Coursera Coach chatbot and AI-assisted grading tools. It's worth noting that this case study is published by Braintrust, the evaluation platform Coursera adopted, so the framing naturally emphasizes the benefits of structured evaluation workflows using their tooling. ## The Problem: Fragmented Evaluation Processes Before establishing a formal evaluation framework, Coursera struggled with several operational challenges common to organizations scaling AI features. Their evaluation processes were fragmented across offline jobs run in spreadsheets and manual human labeling workflows. Teams relied on manual data reviews for error detection, which was time-consuming and inconsistent. A significant pain point was the lack of standardization across teams—each group wrote their own evaluation scripts, making collaboration difficult and creating inconsistency in how AI quality was measured across the organization. This fragmented approach made it challenging to quickly validate AI features and deploy them to production with confidence. ## AI Features Under Evaluation The case study highlights two primary AI features that drove the need for better evaluation infrastructure: **Coursera Coach** serves as a 24/7 learning assistant and psychological support system for students. According to Coursera's internal survey from Q1 2025, the chatbot maintains a 90% learner satisfaction rating. The company reports that users engaging with Coach complete courses faster and finish more courses overall. The chatbot provides judgment-free assistance at any hour, functioning as an integral part of the learning experience. While these metrics are impressive, it's worth noting they come from Coursera's internal measurements rather than independent verification. **Automated Grading** addresses a critical scaling challenge in online education. Previously, grading was handled by teaching assistants (effective but costly) or through peer grading (scalable but inconsistent in quality). The AI-powered automated grading system now provides grades within 1 minute of submission and delivers approximately 45× more feedback than previous methods. Coursera reports this has driven a 16.7% increase in course completions within a day of peer review, though the methodology behind these comparisons isn't detailed in the case study. ## The Four-Step Evaluation Framework Coursera's approach to LLMOps evaluation follows a structured four-step methodology: ### Defining Clear Evaluation Criteria Upfront The team establishes what "good enough" looks like before development begins, not after. For each AI feature, they identify specific output characteristics that matter most to users and business goals. For the Coach chatbot, evaluation criteria include response appropriateness, formatting consistency, content relevance, and performance standards for natural conversation flow. The automated grading system is evaluated on alignment with human evaluation benchmarks, feedback effectiveness, clarity of assessment criteria, and equitable evaluation across diverse submissions. This upfront definition of success criteria is a best practice that prevents teams from moving goalposts or rationalizing subpar results after the fact. ### Curating Targeted Datasets Coursera invests significantly in creating comprehensive test data, recognizing that dataset quality directly drives evaluation quality. Their approach involves manually reviewing anonymized chatbot transcripts and human-graded assignments, with special attention paid to interactions that include explicit user feedback such as thumbs up/down ratings. This real-world data is supplemented with synthetic datasets generated by LLMs to test edge cases and extract challenging scenarios that might expose weaknesses in the system. This balanced approach—combining real-world examples with synthetic data—ensures evaluation covers both typical use cases and edge scenarios where AI systems typically struggle, providing greater confidence that features will perform well across diverse situations. ### Implementing Both Heuristic and Model-Based Scorers Coursera's evaluation approach combines deterministic code-based checks with AI-based judgments to cover both objective and subjective quality dimensions. Heuristic checks evaluate objective criteria deterministically, such as format validation and response structure requirements. For more nuanced assessment, they employ LLM-as-a-judge evaluations to assess quality across multiple dimensions, including response accuracy and alignment with core teaching principles. This dual approach is particularly notable as it acknowledges that not all quality criteria can be reduced to simple programmatic checks, while also recognizing that fully AI-based evaluation can introduce its own biases and inconsistencies. Performance metrics round out the evaluation framework, monitoring latency, response time, and resource utilization to ensure AI features maintain operational excellence alongside output quality. This holistic view—considering both the quality of outputs and the operational characteristics of the system—reflects mature LLMOps thinking. ### Running Evaluations and Iterating Rapidly With Braintrust's evaluation infrastructure in place, Coursera maintains continuous quality awareness through three complementary tracks. Online monitoring logs production traffic through evaluation scorers, tracking real-time performance against established metrics and alerting on significant deviations. This production monitoring capability is essential for catching issues that may not appear in test datasets but emerge in real-world usage patterns. Offline testing runs comprehensive evaluations on curated datasets, comparing performance across different model parameters and detecting potential regressions before deployment. This pre-deployment testing layer provides a safety net before changes reach users. For new features, their rapid prototyping process creates sample use cases in Braintrust's playground environment, comparing different models and testing feasibility before committing to full development. This early experimentation phase allows teams to catch issues quickly, communicate findings clearly across teams, and iterate based on concrete data rather than intuition. ## Practical Example: Detecting Edge Cases in Automated Grading The case study provides a concrete example of how this evaluation framework improved product quality. Early automated grading prototypes focused primarily on valid submissions, which performed well in initial testing. However, through structured evaluation with negative test cases, the team discovered that providing vague or low-quality answers would still result in high scores—a significant flaw that would undermine the educational value of the feature. By going back and evaluating more examples of negative test cases, they identified and addressed this issue, resulting in overall better quality before the feature reached learners. This example illustrates the importance of comprehensive dataset curation that includes edge cases and adversarial examples, not just "happy path" scenarios. ## Results and Organizational Benefits The structured evaluation framework has transformed Coursera's AI development process across several dimensions. Teams now validate changes with objective measures, which the company claims has significantly increased development confidence. The data-driven approach reportedly moves ideas from concept to release faster, with clear metrics supporting go/no-go decisions. Standardized evaluation metrics have created a common language for discussing AI quality across teams and roles, addressing the collaboration challenges that existed when each team used their own scripts. The framework also enables more comprehensive and thorough testing than was previously possible. ## Key Takeaways and Best Practices The case study distills Coursera's experience into actionable recommendations for other organizations: - Starting with clear success criteria before building prevents post-hoc rationalization of results - Balancing deterministic checks for non-negotiable requirements with AI-based evaluation for subjective quality aspects provides comprehensive coverage - Investing in dataset curation that reflects actual use cases, including edge cases where AI typically struggles, improves evaluation reliability - Evaluating operational aspects like latency and resource usage alongside output quality ensures production readiness - Integrating evaluation throughout development rather than treating it as a final validation step catches issues earlier and reduces rework ## Critical Assessment While the case study presents impressive metrics and a well-structured approach, readers should note that this is essentially a customer testimonial published by Braintrust, the evaluation platform Coursera adopted. The specific tooling benefits may be somewhat overstated, though the general principles of structured evaluation—clear criteria, comprehensive datasets, mixed evaluation approaches, and continuous monitoring—are well-established LLMOps best practices regardless of the tooling used. The quantitative results (90% satisfaction, 45× more feedback, 16.7% completion increase) come from Coursera's internal measurements and the methodologies behind these comparisons aren't fully detailed. Nevertheless, the framework presented offers a practical template for organizations looking to bring more rigor to their AI feature development and deployment processes.