PUBLISHED WORK
Frameworks, comparative analyses, and authored methodologies
Original work authored by Maura Randall — built through applied research and hands-on collaboration with AI systems inside real product, team, and leadership constraints.
Comparative Analyses
Neutral, side-by-side comparisons designed to surface tradeoffs, assumptions, and second-order effects — especially the ones that only show up once AI meets real-world constraints.
Prompt vs. Context vs. Collaboration Engineering
The evolution of how we work with AI — from crafting the ask, to designing the inputs, to rethinking the interaction itself. Context engineering is a real step forward. It’s also the midpoint, not the destination.
Read / view →Building the Plane vs. Rebuilding the Cockpit at 30,000 Feet
Teams are already mid-flight. Augmenting them with AI means redesigning collaboration — context systems, decision ownership, trust boundaries, handoffs, and feedback loops — without decelerating delivery.
Read / view →Human-in-the-Loop vs. The Human–AI Loop
Oversight checkpoints vs. continuous collaboration: what changes when humans remain active collaborators throughout the lifecycle.
Read / view →Not All AI Should Be Your Teammate
When “copilot” framing helps — and when it increases cognitive load, reduces trust, and slows teams down.
Read / view →Which Model Is My AI Tool Using?
Model opacity impacts trust, reliability, and product decisions — especially in enterprise and regulated environments.
Read / view →Prompt Engineering vs. Collaboration Engineering
The deeper two-way analysis: why prompt quality alone fails at scale — and how roles, handoffs, feedback loops, memory, and decision ownership change outcomes.
Read / view →Inside the Loop
An authored methodology for building, leading, and learning with AI as a collaborator — with clear roles, trust boundaries, and feedback loops that keep humans in decision ownership.
The Human–AI Loop
The core methodology and library hub — built to make human-led collaboration with AI repeatable and scalable.
View →The Human–AI Triad
A practical team model: distinct roles, differentiated strengths, and a human-led loop that makes collaboration more reliable.
View →The Collaboration Loop
A repeatable workflow for turning ambiguity into clarity — without outsourcing judgment or blurring ownership.
View →The Five Principles of Human–AI Collaboration
The principles that keep the loop transparent, contextual, iterative, human-led, and ethically grounded.
View →The 8 Beliefs Behind the Human–AI Loop Coming soon
The belief system that keeps the loop resilient under real-world constraints — especially leadership pressure and ambiguity.
Draft / in progressFrameworks & Methods
Reusable models and clarity tools that help teams operationalize trust, communication, and realistic expectations with AI.
Anthropomorphism Isn’t the Problem
A more precise take: it’s not “humanizing AI” that breaks teams — it’s unclear collaboration boundaries and misplaced trust.
View →When AI “Forgets”
Memory limits change workflows, reliability expectations, and the emotional experience of collaboration.
View →Why AI Hallucinates
A practical explainer: where hallucinations come from, why they happen, and how to design around them.
View →Impostor Syndrome vs. AI Hallucinations
A human + AI confidence mirror — and a collaboration-first path that helps both teams and tools improve.
View →Tools in Action
Working builds that demonstrate these collaboration patterns in practice — with plain-English context, intent, and usage notes.
Tool Library (GitHub)
Production-ready tools that show how I design human–AI collaboration — prompts, workflows, system patterns, and role-based AI participation.
View the Tool Library →Tool Spotlights Coming soon
Short writeups explaining the collaboration pattern each tool proves — what problem it solves, how it was designed, and where it works (or doesn’t).
Links added as posts go liveThe work behind the work
“For 20 years I built platforms that connected people at scale. The question I’m asking now is the same one — just with a new kind of teammate in the room.”
That question has a methodology now. The Human–AI Loop is where I document what I’ve learned, built, and proven about what humans and AI can achieve together.
Methodology
Guides & Playbooks
Literacy & Writing
Tools & Connect