Automating Domain-Driven Design: Experience with a Prompting Framework

Automating Domain-Driven Design: Experience with a Prompting Framework | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/automating-domain-driven-design-experience-with-a-prompting-framework

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 20
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID automating-domain-driven-design-experience-with-a-prompting-framework | Route /signal-canvas/automating-domain-driven-design-experience-with-a-prompting-framework

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/automating-domain-driven-design-experience-with-a-prompting-framework

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 7

References: 20

Proof: Verification pending

Freshness state: computing

Source paper: Automating Domain-Driven Design: Experience with a Prompting Framework

PDF: https://arxiv.org/pdf/2603.26244v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:58:42.700Z

Signal Canvas receipt window

Not build-ready: Automating Domain-Driven Design: Experience with a Prompting Framework

/buildability/automating-domain-driven-design-experience-with-a-prompting-framework

Ignoreblocked

Subject: Automating Domain-Driven Design: Experience with a Prompting Framework

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
This paper introduces a prompting framework that automates core DDD activities through structured large language model (LLM) interactions.
Implicationpartial
The abstract explicitly states the introduction of a prompting framework for automating DDD activities through LLM interactions.
Verificationpartial
partial
Evidencepartial
We decompose DDD into five sequential steps: (1) establishing an ubiquitous language, (2) simulating event storming, (3) identifying bounded contexts, (4) designing aggregates, and (5) mapping to technical architecture.
Implicationpartial
The abstract clearly lists the five sequential steps that the DDD process is decomposed into by the framework.
Verificationpartial
partial
Evidencepartial
While the first steps consistently generate valuable and usable artifacts, later steps show how minor errors or inaccuracies can propagate and accumulate.
Implicationpartial
The abstract and analysis excerpt both highlight the effectiveness of the early stages of the framework.
Verificationpartial
partial
Evidencepartial
In our evaluation, Steps 1 to 3 worked well, but the accumulated errors rendered the artifacts generated from Steps 4 and 5 impractical.
Implicationpartial
The abstract and analysis excerpt explicitly mention the issues with later steps due to error propagation.
Verificationpartial
partial
Evidencepartial
Overall, the framework excels as a collaborative sparring partner for building actionable documentation, such as glossaries and context maps, but not for full automation.
Implicationpartial
The abstract directly states the framework's utility as a sparring partner and its limitation for full automation.
Verificationpartial
partial
Evidencepartial
Our findings show that LLMs can enhance, but not replace, architectural expertise, offering a practical tool to reduce the effort and overhead of DDD while preserving human-centric decision-making.
Implicationpartial
The conclusion of the abstract directly addresses the role of LLMs in relation to human expertise.
Verificationpartial
partial
Evidencepartial
In a case study, we validated the prompting framework against real-world requirements from FTAPI's enterprise platform.
Implicationpartial
The abstract explicitly mentions the case study used for validation.
Verificationpartial
partial

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Automating Domain-Driven Design: Experience with a Prompting Framework

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