Cascaded AI
Development Workflow
What cascade heat pumps teach us about building with AI
Executive Summary
This post describes a Cascaded AI Development Workflow — a structured, multi‑stage approach to using AI systems in software development where the output of each stage becomes the optimized input to the next.
The core idea is borrowed from cascade heat pump systems in thermodynamics: instead of forcing a single system to do everything inefficiently, we chain specialized stages, each operating in its optimal range. The result is higher overall efficiency, better outcomes, and dramatically reduced cognitive overhead for developers.
"This is not 'prompt engineering.' It is systems design applied to cognition."
The Core Metaphor
How thermodynamics becomes an engineering philosophy
A cascaded heat pump system — each stage operates in its optimal temperature range, passing condenser heat to the next stage's evaporator. Higher effective COP than any single-stage design.
Cascade Heat Pump
- Each stage performs a manageable temperature lift
- Output heat from one stage becomes input energy for the next
- Reaches extreme temperatures with a higher effective COP
- No single compressor operates over the full range
AI Development Cascade
- Each AI stage performs a manageable cognitive lift
- Output from one stage becomes structured input for the next
- Reaches highly executable code with less waste and rework
- No single model operates across too wide a reasoning range
Key principle: Never ask one model, or one human, to operate across too wide a cognitive temperature range.
Why This Matters
Traditional AI Usage Fails Because
- Prompts are too broad
- Context is underspecified
- Architecture and execution are mixed
- Humans are forced to constantly restate intent
- No structural separation of concerns
The Cascade Approach
- Reduces ambiguity early in the process
- Freezes decisions at the correct abstraction layer
- Preserves architectural intent across phases
- Lets developers focus on review, judgment, and integration
- Aligns naturally with Agile and modern architecture practices
The AI Development Cascade — five specialized stages, each performing a focused cognitive lift, passing structured output forward. Entropy decreases at every step.
The Stages
Five intentionally sequential steps — each reduces entropy, freezes decisions at the correct abstraction layer, and hands off a cleaner input to the next.
Goal
Explore the problem space, surface assumptions and constraints, generate a meta-prompt for the next stage.
Artifacts
- A structured prompt designed for planning and architectural reasoning
- Clear articulation of goals, constraints, and non-goals
This stage embraces ambiguity. Nothing is frozen yet.
Goal
Translate intent into a concrete architectural plan. Ask clarifying questions before decisions are locked. Produce a durable, internally consistent blueprint.
Artifacts
- System architecture overview
- Key components and responsibilities
- Data flows and integration points
- Risks and open questions
- Phase decomposition (phases.md)
This stage owns all blueprint artifacts. Nothing downstream exists yet.
Goal
Break the emerging blueprint into ordered, independently buildable phases. Minimize cross-phase coupling early. Create structural boundaries that guide refinement.
Artifacts
- phases.md defining phase goals
- Scope boundaries per phase
- Acceptance criteria for each phase
This file is created early in Stage 2 and evolves alongside the rest of the blueprint.
Goal
Eliminate ambiguity across all architectural surfaces. Ensure consistency between documents. Raise architectural quality before any execution planning begins.
Artifacts
- Polished, internally consistent blueprint documents
- Frozen architecture — no open structural questions
- Consistent terminology and cross-references
Only one document is modified per iteration. This loop continues until architecture is frozen.
Goal
Take a single phase and plan it to completion. Surface remaining unknowns. Generate a build-ready execution plan.
Artifacts
- Step-by-step implementation plan
- Clarifying questions (if any)
- Explicit success criteria
No code is written until this plan is accepted.
Goal
Convert a frozen plan into working code. Minimize backtracking. Keep humans focused on review, testing, and judgment.
Artifacts
- Implemented code
- Tests and documentation
- Phase completion verification
The human role shifts entirely to review, judgment, and integration at this stage.
Design Principles
The invariants that hold every stage together
One Cognitive Lift per Stage
Each step narrows scope and increases specificity. No stage is asked to do more than one type of thinking.
Artifacts Over Chat History
Decisions live in files, not conversations. Every architectural decision is materialized into a durable document.
Freeze Early, Execute Late
Architecture and scope are locked before code generation. Never write code against an unfrozen design.
Human Verification at Phase Boundaries
Humans approve transitions between stages, not every generated token. Senior judgment applied where it matters most.
Entropy Always Decreases
Every stage reduces ambiguity and degrees of freedom. The system converges toward executable, verifiable output.
This Is Not Autonomous Coding
Not prompt spam. Not replacing developers. This workflow amplifies senior engineering judgment by removing unnecessary cognitive load.
Best Suited For
- New feature development
- Complex integrations
- Cross-service architectural work
- High-risk or high-visibility initiatives
Less Useful For
- Trivial bug fixes
- One-line changes
- Highly exploratory spikes
Meta: This Post Was Built With CDF
This essay — including its structure, metaphors, stage definitions, and visual layout — was developed using the Cascaded AI Development Workflow described above. The process began in Stage 1 with ideation and problem framing: clarifying the core insight (cascade heat pumps as a cognitive architecture metaphor), surfacing the key constraints (must be accessible to senior engineers, must not oversimplify), and generating a structured meta-prompt.
That meta-prompt fed into Stage 2, where the blueprint of the post was generated — the section structure, the comparison table, the stage definitions, the principles grid. Stage 2b refined each section for consistency and depth. Stage 4 planned the implementation in Blade/Tailwind with specific animation and interaction targets. Stage 5 built it.
The result is a piece of writing that was shaped by human judgment at every transition — but assembled at a speed and coherence that would have taken significantly longer without the cascade.
"This is how AI becomes an engineering multiplier instead of a distraction."
Summary
The Cascaded AI Development Workflow treats AI as a multi-stage system, not a magic box. By chaining specialized reasoning stages and preserving structure between them, teams can:
- Build faster
- Make fewer architectural mistakes
- Preserve intent across the entire development lifecycle
- Spend more time thinking and less time restating context
References & Further Reading
-
1.
Rankine, W.J.M. (1859). A Manual of the Steam Engine and Other Prime Movers. Richard Griffin and Company.
Foundational thermodynamics work underlying the heat pump cycle.
-
2.
Lorentzen, G. (1995). "Revival of carbon dioxide as a refrigerant." International Journal of Refrigeration, 17(5), 292-301.
Cascade refrigeration system design and efficiency analysis.
-
3.
Bhatt, M.S. et al. (2005). "Simulation and Exergy Analysis of Cascade Heat Pump Systems." Energy Conversion and Management.
COP analysis of multi-stage cascade heat pump configurations.
-
4.
Brooks, F.P. (1975). The Mythical Man-Month: Essays on Software Engineering. Addison-Wesley.
The original argument for separating architectural intent from execution.
-
5.
Martin, R.C. (2017). Clean Architecture: A Craftsman's Guide to Software Structure and Design. Prentice Hall.
Dependency rules and boundary design that inform the phase decomposition approach.
-
6.
Anthropic (2024). Claude's Model Spec — AI systems operating within well-defined context boundaries.
Context window design and staged prompting research.
-
7.
Cursor (2024). Cursor IDE — AI-assisted development tooling used in Stages 2–5.
The IDE-embedded AI that powers the blueprint and execution stages.