Domain Experts vs. Engineers: Feedback Alignment
Explore the critical alignment between domain experts and engineers in AI development to enhance usability and technical performance.
Key Points:
- Domain Experts: Focus on context, usability, and real-world challenges. They define what "correct" means and spot edge cases engineers might miss.
- Engineers: Prioritize technical performance, scalability, and optimization. They ensure systems are efficient and meet measurable benchmarks.
- Challenges: Miscommunication and differing priorities can lead to models that perform well technically but fail to meet practical needs.
- Solutions: Structured feedback processes, collaborative tools, and clear workflows help align both perspectives for better outcomes.
Quick Overview:
- Domain Expert Feedback: Context-driven, focuses on usability and edge cases.
- Engineer Feedback: Metric-driven, emphasizes performance and precision.
- Alignment Strategies: Regular review sessions, parallel feedback systems, and tools like Latitude streamline collaboration.
Aligning these perspectives is critical for building AI systems that are both technically sound and practically useful.
Domain Expert Feedback: Context and Value Focus
Domain expert feedback plays a pivotal role in evaluating large language models (LLMs) by focusing on how well these systems address real-world challenges. Unlike engineers, who often concentrate on technical performance, domain experts bring a practical perspective, ensuring AI outputs align with the problems they’re meant to solve. Their insights bridge technical capabilities with tangible outcomes, offering a fresh lens on system evaluation.
Domain Expert Feedback Characteristics
What sets domain expert feedback apart is its emphasis on context and results. While engineers may prioritize metrics like latency or error rates, domain experts evaluate whether AI systems deliver accurate decisions, inspire user confidence, and drive meaningful improvements in operations. They define what "correct" looks like for a specific task, creating a standard that reflects real-world complexity. For instance, in healthcare, a surgeon might rely on an AI system to analyze data patterns and suggest treatments tailored to diverse patient scenarios. Domain experts also excel at identifying edge cases that standard metrics might overlook.
Domain Expert Feedback Challenges
Despite their critical contributions, domain experts face hurdles when working with AI systems. One common challenge is a lack of technical understanding about which aspects of the model can be adjusted versus those that are fixed. This knowledge gap can make it difficult to turn their qualitative feedback into actionable changes. Additionally, domain experts may struggle to translate their intuitive insights into clear, implementable suggestions for engineers. Another issue arises during the implementation phase, where feedback loops often weaken, leaving domain experts with little insight into how their input is shaping the system. These obstacles highlight the need for better feedback mechanisms.
Best Practices for Domain Expert Feedback
To maximize the value of domain expert input, structured processes are essential. Weekly evaluation sessions, bug triage meetings, and domain-specific audits can help maintain a steady flow of insights. A balanced scorecard approach works well - combining technical metrics with assessments of business outcomes, such as improved decision-making, enhanced user trust, and streamlined workflows. To better understand the expert’s perspective, AI engineers should observe them in action, gaining insights into their decision-making context. Tools that allow experts to flag anomalies, annotate unclear outputs, and spotlight edge cases can further improve collaboration. When discrepancies arise between AI predictions and expert expectations, joint review sessions can clarify whether the issue lies in data quality, model limitations, or genuine edge cases. This fosters an ongoing, collaborative partnership that strengthens system development.
Engineer Feedback: Technical Focus and Optimization
Engineers play a critical role in maintaining the technical foundation of AI systems, ensuring they perform reliably and efficiently. While domain experts prioritize how AI fits into real-world scenarios, engineers zero in on the technical framework that allows these systems to scale. Their feedback is rooted in measurable metrics, optimization strategies, and the structural soundness of the technology.
Engineer Feedback Characteristics
Engineer feedback emphasizes system performance, scalability, and technical precision. Key metrics like latency, throughput, error rates, and accuracy guide their efforts to improve reliability and efficiency. Engineers focus on making systems faster, more modular, and aligned with established benchmarks. Their approach is data-driven, prioritizing quantifiable results over contextual nuances. For example, they address specific issues such as reducing response times, increasing throughput, and optimizing resource use like CPU and memory.
Engineers also design and refine prompts, conduct A/B tests, and monitor performance metrics. They act as a conduit between human intent and machine functionality, fine-tuning the technical instructions that guide AI models. A notable case from June 2023 highlights this process: engineers from OpenAI collaborated with medical experts from Johns Hopkins University to refine over 10,000 medical prompts. This effort resulted in a 28% accuracy improvement for cancer-related queries. This example underscores how engineers’ technical focus drives meaningful advancements.
Engineer Feedback Challenges
Despite their technical expertise, engineers often face challenges when balancing technical metrics with practical application. A common issue is their limited understanding of domain-specific priorities, which can lead to an overemphasis on benchmarks while overlooking real-world utility. As a result, models may excel in controlled tests but fail to deliver relevant or intuitive outputs in practical settings.
Another challenge is interpreting qualitative feedback from domain experts. Engineers may struggle to convert contextual insights into actionable technical changes, creating a disconnect between quantitative data and qualitative assessments. This gap can hinder collaboration and lead to models that miss critical edge cases or fail to address user trust concerns.
Best Practices for Engineer Feedback
To align technical optimization with practical usability, engineers should integrate user-focused metrics - such as satisfaction scores and business KPIs - into their evaluations. This ensures that technical improvements translate into real-world value.
Structured prompt management and testing are essential. Using tools like prompt managers allows engineers to design, test, and refine prompts systematically, avoiding reliance on trial-and-error approaches. Combining diverse evaluation methods, such as LLM-as-judge, human-in-the-loop reviews, and ground truth assessments, provides deeper insights into model performance.
Version control is another vital practice. By meticulously tracking changes, engineers can offer precise feedback on specific iterations, helping cross-functional teams understand how features evolve over time.
Real-time monitoring and debugging in production environments offer critical insights. Engineers can track prompt performance, log errors, and compare different production versions to identify and resolve issues quickly.
Finally, fostering collaboration with domain experts is key. Establishing structured feedback loops with review sessions and annotated audits helps integrate technical and contextual insights. Platforms like Latitude can facilitate this process by creating a space where engineers and domain experts can share evaluations, flag issues, and build consensus, ensuring that both perspectives are aligned for better outcomes.
Comparison: Domain Experts vs Engineers
When it comes to shaping AI systems, domain experts and engineers bring unique perspectives to the table. Their approaches to feedback are shaped by their distinct backgrounds, which influence how they evaluate and refine outputs. While their goals align - improving system performance - their methods and priorities differ, creating both challenges and opportunities for collaboration.
Feedback Comparison Table
The differences between domain experts and engineers become clearer when breaking down their feedback approaches across key dimensions:
| Dimension | Domain Expert Feedback | Engineer Feedback |
|---|---|---|
| Contextual Knowledge | High, deeply tied to industry-specific insights | Moderate, with a focus on technical aspects |
| Technical Precision | Moderate | High, often metric-driven |
| Actionability | Sometimes ambiguous, requiring interpretation | Clear and directly actionable |
| Iterative Alignment | Relies on collaboration and ongoing dialogue | Follows structured optimization cycles |
| Value and Safety Alignment | Strong emphasis on ethics, safety, and business values | Limited unless explicitly defined in requirements |
| Scalability | Requires translation to technical frameworks | Naturally integrated into workflows |
This table highlights the distinct strengths and challenges each group brings, setting the stage for deeper exploration of their feedback dynamics.
Key Comparison Insights
The table above outlines the key differences, but let’s dig deeper into what these mean in practice. The primary distinction lies in knowledge depth and application. Domain experts provide invaluable contextual insights that ensure outputs align with real-world needs, while engineers excel at technical precision and implementation.
Take technical precision and actionability, for example. Engineers often provide feedback that includes clear metrics, measurable criteria, and direct paths for implementation, enabling faster iterations. On the other hand, domain experts may offer feedback that, while critical for practical utility, often needs to be translated into actionable engineering tasks. This translation can slow progress initially but is crucial for aligning outputs with user needs and business goals.
The scalability dimension reveals another key difference. Engineers naturally consider scalability, embedding it into their workflows to handle increased complexity and load. Conversely, domain experts focus on whether solutions meet specific contextual requirements, which may require further technical adaptation to achieve broader scalability.
Value and safety alignment is where domain experts shine. Their feedback often emphasizes ethical standards, safety, and alignment with industry-specific values. Engineers, unless explicitly guided by technical specifications, may overlook these aspects, focusing instead on achieving technically sound outputs. This gap can lead to solutions that meet performance metrics but fall short in practical relevance.
Here’s an example: Engineers developed a fraud detection model that performed well on technical metrics, identifying suspicious transactions with high accuracy. However, domain experts flagged a critical issue - the system misclassified payroll-related transaction spikes as fraudulent, ignoring the business cycle context. By combining the engineers’ technical expertise with the domain experts’ contextual knowledge, the team refined the model to address both technical performance and practical application.
Finally, there’s the iterative alignment process. Domain experts often rely on collaborative feedback loops, engaging in ongoing discussions to refine outputs. Engineers, on the other hand, prefer structured optimization cycles with defined benchmarks. While each approach has its strengths, combining them creates a feedback loop that balances technical rigor with real-world relevance.
Together, these insights underscore the value of blending the complementary strengths of domain experts and engineers. By recognizing and leveraging these differences, teams can develop structured feedback workflows that drive both technical excellence and practical utility.
Feedback Alignment Strategies: Building Collaborative Workflows
Structured workflows help bridge the communication gap between domain experts and engineers, turning diverse perspectives into better-performing models. By focusing on role-specific practices, the following strategies aim to streamline cross-functional feedback and collaboration.
Collaborative Feedback Frameworks
To achieve effective feedback alignment, teams need more than casual conversations - they require structured and repeatable processes. Three proven frameworks stand out in the development of large language models (LLMs): iterative calibration cycles, layered feedback alignment, and profile-aware workflows.
Iterative calibration cycles create regular opportunities for teams to review model outputs together. These sessions allow both engineers and domain experts to identify issues, resolve disagreements, and align on improvements. For many teams, weekly evaluation meetings strike the right balance, providing enough time to implement changes while maintaining progress.
Layered feedback alignment separates technical concerns from domain-specific issues. This approach enables engineers to focus on optimizing performance metrics and system reliability, while domain experts refine business logic and contextual accuracy. By addressing feedback in parallel, teams avoid bottlenecks and ensure that one type of feedback doesn’t delay the other.
Profile-aware workflows customize feedback forms to match each group's expertise. Domain experts concentrate on business outcomes, while engineers assess technical metrics. This tailored approach ensures that both groups contribute effectively without losing sight of the overall picture.
For example, this method helps prevent misclassifications while allowing engineers to implement technical updates as domain experts validate additional business scenarios.
Consensus-Building Methods
Even with structured frameworks, disagreements are bound to occur. What separates high-performing teams from others is how they handle conflicts and build consensus around tough decisions. While frameworks facilitate regular collaboration, consensus-building techniques ensure disagreements are resolved constructively.
Transparent documentation of decisions in a shared system accessible to all team members is essential. This creates an audit trail, making it easier to revisit past discussions and understand the rationale behind decisions when similar issues arise.
Using evaluative layers provides objective criteria for resolving conflicts. Instead of relying on subjective preferences, teams can test different approaches against both technical metrics and domain-specific goals. For instance, engineers may favor one method based on performance data, while domain experts might prefer another for its alignment with business logic. Evaluative layers help determine which approach delivers better results in practice.
Conflict resolution protocols establish clear steps for addressing impasses. These could include facilitated workshops led by neutral parties or structured decision-making processes that weigh technical and domain considerations systematically. Joint reviews of cases where AI predictions differ from expert-labeled data can also uncover underlying assumptions, helping teams agree on the best outcomes.
By making these processes routine rather than exceptional, teams can ensure that conflict resolution remains collaborative and productive.
How Latitude Supports Feedback Alignment
The strategies outlined above align closely with the goal of combining practical and technical insights for effective LLM development. Latitude’s tools are specifically designed to enhance structured collaboration, ensuring that all feedback is both measurable and actionable.
Latitude provides shared workspaces where feedback and results are accessible to everyone, breaking down information silos that often hinder alignment. Its evaluation features enable teams to test conflicting feedback against measurable outcomes, moving discussions beyond subjective arguments. Techniques like LLM-as-judge, human-in-the-loop evaluations, and ground truth comparisons allow teams to assess approaches based on real performance data.
Version control for prompts and agents ensures every change is documented with clear explanations. This transparency makes it easier to track how feedback evolves into implementation decisions and offers a reliable audit trail for revisiting past choices.
Real-time monitoring and debugging tools give both engineers and domain experts a shared view of system performance. When issues arise, teams can collaboratively analyze the same data, identify problems, and validate solutions based on actual usage patterns.
"Latitude is spot-on, plus you get logs, custom checks, even human-in-the-loop. Orchestration and experiments? Seamless. We use it and it makes iteration fast and controlled." – Alfredo Artiles, CTO, Audiense
Pablo Tonutti, Founder of JobWinner, shared a similar experience:
"Tuning prompts used to be slow and full of trial-and-error… until we found Latitude. Now we test, compare, and improve variations in minutes with clear metrics and recommendations. In just weeks, we improved output consistency and cut iteration time dramatically." – Pablo Tonutti, Founder, JobWinner
The ability to iterate quickly, backed by clear metrics and shared visibility, demonstrates how the right tools can turn feedback alignment into a competitive edge.
Conclusion: Achieving Effective Feedback Alignment
Key Takeaways
Creating successful large language models (LLMs) requires a partnership between domain experts and engineers, each bringing vital strengths to the table. Domain experts contribute deep contextual knowledge, practical constraints, and business insights that ensure AI systems are useful in real-world settings. On the other hand, engineers provide the technical know-how to design scalable, efficient systems. Neither group can achieve production-ready LLM features on their own - it’s the collaboration between the two that builds user trust and adoption.
Strong collaboration hinges on mutual understanding. When engineers grasp the nuances of a domain and experts appreciate technical challenges, teams avoid communication breakdowns and misaligned goals. This shared understanding ensures feedback is meaningful and actionable, saving time and resources while driving better outcomes.
Structured frameworks turn collaboration into measurable success. Regular evaluation sessions and clear feedback processes create consistent opportunities for teamwork, rather than relying on chance. Successful teams also establish methods for resolving disputes and maintain transparent documentation, leaving a trail of decisions that can guide future efforts. These frameworks foster a forward-thinking, unified approach to AI development.
The results are clear: organizations that prioritize collaborative feedback between engineers and domain experts consistently deliver more impactful AI solutions. This collaboration demonstrates how structured, shared feedback can lead to tangible improvements.
Final Thoughts on Feedback Alignment
Continuous collaboration is the key to long-term success. Feedback alignment isn’t a one-time effort - it evolves alongside advancements in technology and shifts in organizational priorities. As LLM capabilities grow and deployment practices mature, teams must stay flexible while remaining committed to collaborative workflows.
Organizations that embrace co-creation over siloed handoffs will lead the way. Teams that prioritize sustained, productive alignment will outperform those working in isolation. Achieving this requires more than just good intentions - it demands the right tools and processes to support ongoing collaboration.
Platforms like Latitude exemplify how structured feedback can be seamlessly integrated into development workflows. With features like shared workspaces, transparent version control, and real-time monitoring, Latitude helps domain experts and engineers stay aligned at every stage of the development process.
While LLM development will continue to evolve, the need for aligned feedback between technical and domain perspectives will only grow. Teams that master this alignment now are laying the groundwork for the most successful AI applications of the future.
FAQs
How can structured feedback processes help domain experts and engineers work better together in AI development?
Structured feedback processes act as a bridge between domain experts and engineers, creating a clear and consistent way for both teams to stay aligned on goals and expectations. By setting specific criteria for feedback - like measurable outcomes or detailed use cases - teams can minimize misunderstandings and work together more efficiently.
These processes also promote collaboration by offering a shared framework for assessing AI systems, spotting issues, and refining solutions. The result? Faster development cycles and a final product that aligns well with both technical needs and domain-specific demands.
What challenges do domain experts and engineers face when aligning feedback for AI systems, and how can they overcome them?
Domain experts and engineers often face hurdles when trying to align their feedback, largely because of differences in expertise, communication approaches, and priorities. While domain experts typically focus on overarching goals or specific industry needs, engineers tend to zero in on technical feasibility and system performance. These contrasting perspectives can sometimes lead to misunderstandings or mismatched expectations.
The solution lies in promoting collaboration and ensuring clear communication. Regular discussions can help close knowledge gaps, allowing both sides to better understand each other's constraints and objectives. Tools like Latitude can play a crucial role here, offering a platform that supports seamless collaboration and provides resources to co-develop and refine AI features. By emphasizing transparency and shared goals, teams can work together to build AI systems that are both effective and aligned with their collective vision.
How does Latitude help domain experts and engineers align their feedback effectively?
Latitude simplifies teamwork between domain experts and engineers by offering tools that make it easier to align feedback and create production-ready LLM features. It acts as a bridge, connecting technical knowledge with specialized expertise to improve communication and streamline workflows.
With its collaborative workspace, Latitude enables teams to work more efficiently, iterate quickly, and deliver high-quality AI solutions designed to meet practical, real-world demands.