Javier Marín

Why Structured Outputs Break LLM Performance

Javier Marin

Applied AI Consultant | Applied AI Researcher

Quantify how XML/JSON degrades emergency detection 100%→79% through systematic testing. Infrastru

Measure Format Constraint Degradation on Edge Cases

dentify when 86% overall accuracy masks 21% false negatives on safety-critical cases. Measure what matters, not what's e

Diagnose When Validation Metrics Mislead

Explain probability mass competition in fixed-size windows. Why more context degrades edge cases. Architectural constrai

Understand Attention Competition Mechanism

Learn to achieve reliable AI outcomes without prompt packs, system prompts, or agent tricks—use structured inputs for consistent results.

Creator, AIgogy Framework™ | Advisor

Rockford University

Silali Banerjee

Creator of AIgogy Framework™ for reliable, structured AI outcomes

July2026Cohort

Pretty much anyone using an AI chatbot has gone through the frustration of a response that isn't exactly what they wanted. Problems include hallucinated, drifted, generic, and inconsistent output. Sometimes all of these happen; other times, one or more.People try prompt improvisation, prompt tweaking, prompt packs, prompt libraries, trial and error, tries and retries. Sometimes it works; other times, it does not.Too many details in a prompt cause prompt bloating and do not solve the problem. Adding context helps, but too much context creates problems.AI agents and skills do not solve this problem with open-ended work either.People give up, thinking LLMs are probabilistic and cannot work better.That is not true.It may never be as deterministic as systems in terms of consistency and reliability, but it can be mostly consistent and reliable.Similarly, users can create a path with an LLM using the right framework. Users can guide their request through the LLM toward their desired outcome by using the right framework.Learn to use this science-backed, proven framework.It is based on research (<a target="_blank" rel="noopener noreferrer nofollow" href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6516618">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6516618</a>) and has been tested in practice.<hr>

User's framework for moving beyond trial-and-error prompting with any LLM.

Achieve More Reliable AI Outcomes

Reflect Your Voice and Working Style

Embed Your Judgment and Standards

Reduce Rework and Increase Leverage

Leaders (Decision Owners) Leaders accountable for outcomes who need AI to reflect their judgment without reviewing or correcting every output

Operators (Execution Owners) Operators responsible for consistent delivery who need AI to produce aligned outputs without repeated rework

Knowledge Workers (Expertise Carriers) Experts who need AI to apply their domain knowledge and standards, not generate generic outputs.

Decisions made without shared criteria lead to inconsistency and bias

Educator, Content Creator and ex-Founder

PASHA Holding LLC

IU Internationale Hochschule

HWZ Hochschule für Wirtschaft Zürich

Census

Matthew Brandt (Matty)

Educator, Content Creator and ex-Founder with 10+ years in Data Analytics

Jun 5th

Your organisation either claims to be data-driven or has it written broadly across your internal documentation. Yet, you likely still struggle with inconsistent, slow, or biased decisions. The root problem isn’t a lack of data - you have heaps of it - it’s a lack of clearly defined decision criteria. When teams rely on intuition, implicit and unspoken rules, outcomes vary depending on who is in the room, priorities shift unpredictably, and alignment breaks down.In this workshop we will tackle that gap directly. By building a structured Decision Criteria Catalogue, you create a shared language for how decisions are evaluated across the organisation. This reduces ambiguity, speeds up decision-making, and makes trade-offs explicit rather than political.More importantly, over time, your catalogue allows for the creation of a transparent Decision Log, demonstrating the effectiveness of your criteria.In three hours, you will draft your own Decision Criteria Catalogue, have it challenged by peers, and leave with something practical to take back and implement with your colleagues.

You lead an analytics team and want decisions to consistently reflect your insights, not just dashboards.

You make frequent decisions and want faster, clearer, and more defensible outcomes.

You align teams across functions and want consistent, transparent decision-making.

Master the systems to stop micromanaging and transition from a referee to an orchestrator of a self-running team.

Founder at The Strength Hack

Daimler AG

Airbus

Audi

Toll Collect GmbH

Murk Creusen

Founder & Leadership Coach at Strength Hack

Founding Cohort

You cannot fix burnout by working harder. You have a matching problem.AI is making your team's hard technical skills obsolete faster than they can learn them. You cannot fix this "AI anxiety" or burnout by asking them to work harder. You don't have an effort problem; you have a matching problem.Most Engineering Managers spend 70% of their week trapped in the "Mediocrity Loop" - refereeing personality clashes, fixing broken handoffs, and watching their team's shared knowledge collapse as junior roles vanish. You're bleeding runway and morale.This 30-day accelerator gives you the system to break out. Using the Big Five (or CliftonStrengths) assessment, a proven framework, and the Strength Hack AI, you'll map your team's cognitive architecture and engineer the conditions for psychological safety - not just hope for it.In 4 weeks, you will:<ul><li>Stop Generalist Exhaustion: Identify energy leaks and delegate based on Cognitive Fit, not calendar space. </li><li>Engineer Swift Trust: Use the 6-dimension Matching Compass to translate friction into synergy.</li><li>Drive ROI: Transition from a reactive referee to a strategic orchestrator</li></ul>

Engineer Psychological Safety. Stop Refereeing Your Team.

Shortcut the "Storming" Phase to Engineer Swift Trust

Reclaim 70% of Your Week (Plug the Energy Leak)

Turn Personality Clashes into Solvable Data

Restore the "Who Knows What" Architecture Your Team Lost

Walk Away with a "Plug-and-Play" Facilitation Kit

To get ROI from this course, a real-world team (direct reports or agile squads) is recommended to apply the Matching Compass frameworks.

Team Leadership or Management Role

You must take the free Big Five test (or CliftonStrengths) before to map your personal psychological operating system.

Completion of a Baseline Assessment

The Overwhelmed Engineering Manager / Tech LeadYou lead a hybrid or remote team. You want a data-driven system, not another workshop.

The Ambitious Senior / Staff Engineer<blockquote>You're moving to people manager. You want proven frameworks to lead a team without micromanaging.</blockquote>

The People-Conscious CTO / CPO<blockquote>You own culture and delivery for 10–200 people. Your board wants AI readiness. You need L&amp;D that's credible.</blockquote>

Bad Learning Design Is Costing You Customers — Here's the Science Behind It

Learning Scientist | L&D Strategist

Vanorsdale Learning Lab

Great Game of Business

Docebo

National Alliance on Mental Illness Florida

English Like a Native

Dr. Christie Vanorsdale, Ed.D, Ms.Ed.

Researcher. Practitioner. 15 years translating learning science into results

Cohort 1

You've updated the onboarding. Rebuilt the knowledge base. Added walkthrough videos, help articles, live training sessions. Your customers still aren't adopting the product the way you need them to.The instinct is to fix the content. More of it. Make it Better, or Shorter. More visual. But, the problem isn't the content.It's that content was never designed to produce learning in the first place.Human beings don't retain complex knowledge because they watched a video or completed a walkthrough. Retention requires intentional sequencing, cognitive scaffolding, and an architecture that reduces friction rather than creates it. Most customer onboarding violates every one of those principles — not because the people who built it weren't smart, but because they were never taught what learning science actually says about how knowledge transfers.This workshop gives founders and leaders the diagnostic framework to identify exactly where their customer education breaks down — and the research-backed principles to fix it. You'll leave with a clear line of sight between learning architecture and the revenue outcomes you actually care about: adoption, retention, and renewal.

Learn the science behind knowledge transfer and leave with a diagnostic framework that connects learning architecture to business outcomes.

Understand why customers aren't retaining your product using learning science

Pinpoint exactly where your customer onboarding breaks down

Apply three evidence-based principles to improve product adoption

Connect learning design decisions to churn, adoption, and renewal.

The Scaling Founder Complex product. Customers churn before reaching value. Onboarding feels broken but there's no framework to diagnose why

The Customer Success Leader Owns onboarding and renewals. Content exists but adoption is flat. Needs a framework and internal buy-in.

The L&amp;D Manager Fixing customer education with no infrastructure or alignment. Knows training theory. Missing the revenue impact language