Task Complexity Assessment

You're asking the AI to do too much (at once). LLMs struggle with complex, multi-faceted tasks due to competing objectives, broad success criteria, and the cognitive load of handling multiple concerns simultaneously. This leads to higher variance in outputs, reduced reliability, and difficulty in identifying where failures occur in the reasoning chain. Effective AI Engineering Tip #21: Break Complex Tasks into Evaluable Components 👇 The Problem ❌ Many developers approach complex tasks by cramming everything into a single, comprehensive prompt: [Code example - see attached image] Why this approach falls short: - Poor Evaluation Granularity: When the LLM gets sentiment right but categorization wrong, you can't measure individual component performance or identify specific failure points. - Conflicting Objectives: Generating empathetic responses while maintaining factual accuracy creates competing priorities that reduce overall quality. - High Variance: Complex tasks have too many "acceptable" paths to completion, leading to inconsistent outputs and unpredictable behavior. - Difficult Debugging: When something goes wrong, it's nearly impossible to identify which specific component failed or needs improvement. The Solution: Task Decomposition (Prompt Chaining) ✅ A better approach is to break complex tasks into focused, individually evaluable components. This technique, also called "prompt chaining," creates a pipeline where each step has a clear, measurable objective and feeds structured output to the next component. [Code example - see attached image] Why this approach works better: - Individual Evaluation: Each component can be tested and measured separately. Poor categorization doesn't mask good response generation, allowing targeted improvements. - Focused Objectives: Each LLM call has a single, clear purpose, reducing conflicting requirements and improving consistency within each component. - Model Optimization: Use faster, cheaper models (gpt-4o-mini) for structured analysis tasks and reserve powerful models (gpt-4o) for creative response generation. - Easier Debugging: When issues arise, you can pinpoint exactly which component failed and iterate on just that piece without affecting the entire system. The Takeaway ✈️ Don't try to solve complex, multi-objective tasks in a single LLM call. Task decomposition through prompt chaining creates more reliable, evaluable, and maintainable AI systems. Each focused component can be individually optimized, tested, and improved, leading to better overall performance and easier debugging when things go wrong.

“Did the user complete the task? Yes? Great!” Task analysis is often treated like a checkmark. But this approach overlooks the complex mental, emotional, and sensory activity happening even in the most basic flows. Users are not just clicking through, they’re thinking, feeling, and reacting at every step. In a five screen flow, even one that looks simple and linear, users do a lot behind the scenes. They may make 50 to 100 small decisions, like noticing a button, figuring out where to tap, or checking if something looks right. A lot is going on in someone’s head. They’re also pulling from memory. Maybe they remember a password, what was on a previous screen, or are trying to guess what will happen next. That could happen 10 to 20 times in just a few moments. Emotions are involved too. A confusing step might create frustration. A smooth one could bring relief or satisfaction. You might see 5 to 10 emotional spikes, both positive and negative, as users experience: → perception → attention → memory → decision-making → predictions → motor control → context-switching → goal-tracking → self-monitoring Their minds are shifting constantly, like switching from browsing to making a payment, which takes energy and focus. So even if a task looks easy on the surface, there’s a lot going on underneath. That’s why task analysis alone isn’t enough. Testing concepts in high volume reveals much more. Using UX metrics to track emotional highs, effort, and behavior helps you see a fuller picture. These metrics give you stronger signals to guide better design decisions. #productdesign #productdiscovery #userresearch #uxresearch

Competencies & Leadership Spans as Design Criteria for a Procurement Organisation: The illustration shows an integrated competency model for procurement as well as a practice-oriented method for determining the optimal leadership span. The upper part shows a comprehensive understanding of roles and competences that takes into account both personal (soft skills) and professional (hard skills) abilities. These competences are structured according to typical roles in procurement - from the operational buyer through tactical and strategic functions to the head of procurement. For each role, specific skills are named that are necessary for successful fulfilment. In operational procurement, the focus is on system knowledge, understanding rules and ordering processes. As responsibility increases, the requirements shift towards topics such as negotiation skills, contract management, strategic analyses and project planning. At management level, leadership and change skills dominate, such as team leadership, coaching, change management or organisational development. The visualisation makes it clear how the requirements in procurement develop along the career paths and how important it is to systematically build and develop both personal and professional skills. The lower part of the figure provides a sound method for determining the appropriate management span. The complexity of a management task can be systematically assessed using seven relevant criteria - including functional diversity in the team, degree of standardisation of processes, number and intensity of external and internal interfaces, degree of maturity of the projects handled and geographical distribution. Each criterion is assessed on a four-point scale that reflects the degree of complexity. The assessment results in an overall value that can be translated into a recommendation for the optimum management span. The model shows that a smaller management span makes sense for highly complex tasks (e.g. diverse tasks, many interfaces, international collaboration), while larger teams can be managed efficiently for standardised, homogeneous tasks. The recommended management span ranges from 6 to over 20 employees. The model supports purchasing organisations in designing their management structure in a targeted and appropriate manner - especially in times of growing requirements and increasing task diversity. It contributes to the professional management of procurement functions and relieves the burden on managers. Dr. Mario Büsch, Purchnet.de

Ever wonder how we can better understand user tasks and create designs that truly support users in accomplishing their goals? Task analysis is a powerful technique that can help us break down tasks, understand user goals, and inform design decisions early in the product design lifecycle. In UX projects, task analysis helps us evaluate how effectively an interface enables task completion by breaking down user actions into steps. This process allows us to uncover hidden complexities, even in simple tasks, and identify where users might make mistakes or face challenges. So, how do you conduct a task analysis? Here’s a quick overview: 1. Collect information about the task: Understand your users, their goals, and how they currently accomplish the task. 2. Describe the user’s goal: Identify the start and end points, and place the goal at the top of the hierarchy. 3. Split the user’s goal into sub-goals: Break the task into actionable sub-goals. 4. Break each sub-goal into a sequence of steps: Include mental and physical actions required to complete each sub-goal. 5. Inspect the hierarchy of the task analysis for design opportunities: Look for errors, inefficiencies, and opportunities for improving the design. By integrating task analysis early in your design process, you can define user goals, evaluate task completion, and identify design opportunities that improve user efficiency and effectiveness. How do you use task analysis in your UX projects?