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REVIEW 4 major objections 6 minor 41 references

System-driven Cloud Architecture Design Support with Structured State Management and Guided Decision Assistance

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read CloudArchitectBuddy replaces free-form chat with explicit state and a guided question loop, and 16 practitioners rated it easier to use while design quality stayed comparable to ChatGPT.

desk verdict A competent HCI study of a sensible system-driven cloud architecture assistant, but the design-quality equivalence claim is under-supported by the disclosed methods. read the letter →

arxiv 2505.20701 v2 pith:3XIDVSHP submitted 2025-05-27 cs.SE cs.HC

Ryosuke Kohita , Akira Kasuga This is my paper
classification cs.SEcs.HC
keywords CloudArchitectureSoftwareDesignLargeLanguageModelsSupportSystemsHuman-AIInteractionStructuredStateManagementGuidedDecisionAssistanceUsabilityEvaluation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

CloudArchitectBuddy is a cloud architecture design assistant that replaces open-ended chat with a structured, system-guided workflow. It keeps two explicit state models—one for user requirements and preferences, one for the proposed architecture and its evaluation—and drives the design through proposal, summarization, inspection, and targeted yes/no questions. In a study with 16 industry practitioners across four AWS and GCP scenarios, the tool produced designs of comparable quality to ChatGPT while scoring higher on ease of use and likelihood to recommend. The paper's central claim is that explicit state representation plus guided decision assistance can support architectural reasoning as well as free conversation does, and that adding chat-style input would combine the strengths of both.

What carries the argument

The load-bearing mechanism is the iterative state-update loop between UserState and ArchitectureState. UserState has Subject (the initial requirements) and Preferences (answers, evaluations of alternatives, and services pinned as mandatory), while ArchitectureState has Services, Summary, Inspection, and Inquiry. In each cycle the system proposes services, writes a summary with a diagram and quality notes, inspects the design for issues and alternatives, and asks prioritized yes/no questions; the user's answers update Preferences, and the loop repeats with the previous architecture as context. This makes the design state visible and lets the LLM refine incrementally, which the paper argues is what preserves quality while reducing cognitive load.

What would settle it

Have two or more independent infrastructure experts, blind to which tool produced each design, score the same outputs with the full rubric; if their scores disagree substantially, or if a blinded panel consistently ranks ChatGPT designs higher on a validated quality scale, the comparable-quality claim would fail.

Watch

Extended reading notes

Core claim

The paper claims that making the design process explicit and system-driven is a viable alternative to LLM chat for cloud architecture design. With CloudArchitectBuddy, every iteration starts from a visible UserState and produces a visible ArchitectureState: proposed services, a summary with a diagram and quality notes, an inspection listing issues and alternatives, and prioritized inquiries. Sixteen practitioners using this workflow on four realistic scenarios matched the architectural quality of ChatGPT outputs and gave the structured tool higher usability and recommendation ratings. The paper concludes that structured state management improves design understanding, that guided questions reduce cognitive effort and surface missing requirements, and that a hybrid with free-text chat would be the practical next step.

Load-bearing premise

The result that design quality is comparable depends on the experts' three-level rubric being a fair measure of cloud architecture quality; the paper does not publish the full rubric or check whether independent judges would score the same outputs consistently.

Editorial extensions

If this is right

  • If the result holds, architecture design tools can offer LLM-based quality without forcing users to manage a long, unstructured chat thread.
  • The Inspection and Inquiry steps give users a built-in checklist of requirement gaps, which should reduce overlooked concerns compared with free-form prompting.
  • Because the same state loop ran on both AWS and GCP scenarios, the approach appears portable across cloud providers rather than tied to one service catalogue.
  • The qualitative feedback points to a hybrid interface—guided states plus free-text chat—as the next design target, which the paper explicitly proposes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension the paper leaves implicit is that the explicit state could serve as an auditable design record, letting teams replay why each service and preference was chosen—something chat logs do not naturally provide.
  • If structured state is what carries the quality, then varying the amount of system guidance in a controlled experiment would reveal whether the usability gain is monotone or peaks at a particular level of structure.
  • The scoring rubric is the main open measurement gap; a follow-up with multiple independent expert judges, blind to which tool produced each design, would turn the 'comparable quality' result from a trend into a firmer claim.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper presents CloudArchitectBuddy (CA-Buddy), a system-driven cloud architecture design support tool that combines structured state management (UserState and ArchitectureState) with guided decision assistance. The system is evaluated in a role-playing study with 16 industry practitioners who used CA-Buddy and ChatGPT to design cloud architectures for four scenarios. The paper reports comparable design quality to ChatGPT, higher ratings for ease of use (marginally significant) and recommendability (not significant), and qualitative benefits such as improved architecture visibility and systematic identification of requirement gaps. Based on user feedback, the authors propose integrating a chat interface into the structured workflow as a future direction.

Significance. If the evidence were fully convincing, the paper would provide a useful empirical data point on the trade-offs between structured, system-driven design support and free-form chat interfaces for cloud architecture design. The system description is concrete, the prompt templates are included in Appendix A, and the thematic analysis of user feedback (Table 6) is informative. The authors are candid about limitations, including reliance on static LLM knowledge and observed proposals of deprecated services (Section 6). However, the central claims—comparable design quality and higher usability—rest on statistical evidence that is weaker than the abstract suggests, and the design-quality rubric is not disclosed. The study is small (n=16) but has a counterbalanced design and mixes quantitative and qualitative measures; these are strengths that partially offset the inferential limitations.

major comments (4)
  1. [Section 4.2.1, Table 4] The claim of "comparable architectural quality" is not supported by the reported statistics. The paper interprets non-significant Mann-Whitney tests as evidence of equivalence, but non-significance does not demonstrate equivalence. No equivalence margin, confidence intervals, or effect sizes are reported. Moreover, the raw scores show several notable gaps favoring ChatGPT (e.g., D Traffic Handling 3.0 vs 2.5, D Chat Feature 2.75 vs 2.0, D Runtime Setup 2.75 vs 2.125, D UI Delivery 2.875 vs 2.375), and the overall win count is 13 vs 10 in ChatGPT's favor. The abstract's "comparable design quality" should be tempered to "no statistically significant difference was found" and supported by appropriate equivalence testing or at least effect-size reporting.
  2. [Section 4.1 (Evaluation) and Table 4] The design quality scoring rubric is not disclosed. The text only states that infrastructure experts identified key services and classified them into Level 1/2/3, and that each solution received a score on a 3-point scale. The full criteria, the rating procedure, and the identity or qualifications of the raters are absent. No inter-rater reliability measure (e.g., Cohen's kappa) is reported. Without this information, the scores in Table 4 cannot be independently verified, and the comparison between CA-Buddy and ChatGPT may reflect rater inconsistency or bias. This is load-bearing because the central claim of design-quality parity rests entirely on this unvalidated instrument.
  3. [Section 4.2.2, Table 5] The usability advantage is overstated. Only "ease of use" shows a marginally significant trend (p<0.10); "frequency of use" and "recommendation likelihood" show no statistically significant difference. The abstract states "participants rated our system higher for usability" and the conclusion describes the system as "easier to use and more recommendable," but the data only support a weak trend on one of three usability dimensions. The authors should either report full inferential statistics for all three dimensions and discuss the null results, or soften the claim to "participants rated ease of use somewhat higher."
  4. [Section 4.2.1] The multiple per-topic Mann-Whitney tests (25 comparisons across the four scenarios) are reported without any correction for multiple comparisons. With 25 tests at alpha=0.05, one significant result and two trends are within the range of chance. The statement "only the chat feature topic showed a statistically significant difference" is also inconsistent with the table, which marks two additional topics (Traffic Handling, Runtime Setup) as trends (p<0.10). The authors should report adjusted p-values or explicitly acknowledge the exploratory nature of these per-topic tests.
minor comments (6)
  1. [Footnote 2] The URL "https://comming.soon.com/cloudarchitectbuddy" contains a typo ("comming") and is a placeholder; the open-source availability statement cannot be verified. Please correct or remove the URL until the repository is actually public.
  2. [Section 4.1 Procedure versus Evaluation] The procedure states participants designed using "either" CA-Buddy or ChatGPT, while the evaluation section says participants evaluated "both tools." Please clarify whether the design was within-subject or between-subject, and describe the counterbalancing order (e.g., how many started with each tool and how scenarios were assigned).
  3. [Section 3] The certification exam questions are from "privately sourced preparation materials," which makes the preliminary evaluation non-reproducible. Consider using publicly available sample questions or releasing the compiled set as supplementary material.
  4. [Table 4 footnote] The definition of asterisks says "significant difference (p <0.05)" and "trend (p <0.10)" but does not state that these are uncorrected p-values. Add this detail and note the inconsistency with Section 4.2.1, which says only the chat feature topic was significant.
  5. [Section 2.4] "the currentUserState" should read "the current UserState"; please also check for other formatting issues such as missing spaces in the text.
  6. [Figure 3] The panel references in the text ("Panel (1) and (3)") are difficult to map to the figure; consider adding explicit panel numbers in the figure itself.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the evaluation is an empirical comparison against an external baseline, not a derivation from fitted inputs or self-cited premises.

full rationale

The paper's central contribution is a system (CA-Buddy) with structured state management and guided decision assistance, evaluated empirically against ChatGPT. There is no formal derivation chain, no fitted parameter that is later renamed as a prediction, and no load-bearing self-citation. The design-quality comparison uses an expert-developed rubric with a three-level service classification, applied independently to designs produced by both tools; the rubric is not derived from CA-Buddy's outputs, and the system's design choices (stated fields, prompt templates, workflow steps) are fixed and not fitted to the evaluation data. The usability ratings come from participant Likert responses, which are independent of the system's internal state model. The paper's acknowledged limitation that the rubric is summarized rather than fully disclosed, and that inter-rater reliability is not reported, is a validity concern about the measurement instrument, not circularity. No equation, definition, or citation reduces the paper's claims to its own inputs. Therefore the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 2 invented entities

No free parameters are fitted in this empirical study. The central results rely on four domain assumptions about LLM output reliability, the validity of the expert scoring rubric, the transferability of role-playing to practice, and the stability of LLM responses. The two invented entities are software design constructs, not physically postulated entities.

assumptions (4)
  • domain assumption The GPT-4o-based LLM can generate plausible cloud architectures, summaries, inspections, and follow-up questions without human correction.
    The entire CA-Buddy pipeline in Section 2.4 and the prompts in Appendix A assume the LLM outputs are usable as-is for design support.
  • domain assumption The expert-developed three-level scoring rubric (Level 1/2/3 services) is a valid and reliable measure of cloud architecture quality.
    Section 4.1 Evaluation says infrastructure experts developed criteria, but the full rubric and any inter-rater reliability check are not reported; all quality scores depend on this.
  • domain assumption Role-playing as lead engineers in a 15-minute task adequately represents real cloud architecture design behavior.
    Section 4.1 Procedure describes the role-playing design; the generalizability of the usability findings to real projects rests on this assumption.
  • domain assumption LLM response variability does not dominate the observed tool differences.
    Only single runs of gpt-4o and ChatGPT are reported; the attribution of differences to the interface rather than to LLM nondeterminism assumes output stability.
invented entities (2)
  • UserState
    purpose: Structured representation of evolving user requirements and preferences that the system proposes designs from.
    The construct is central to the proposed workflow but has no falsifiable handle outside the authors' implementation and the small user study.
  • ArchitectureState
    purpose: Structured representation of the proposed services, summary, inspection, and inquiry at each design iteration.
    This is a design abstraction introduced by the paper; its benefit is supported only by self-reported usability data from 16 participants.

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Cite this review

Pith. "Pith review of System-driven Cloud Architecture Design Support with Structured State Management and Guided Decision Assistance." pith.science (2026). https://pith.science/paper/3XIDVSHP

@misc{pith2026250520701,
  author       = {Pith},
  title        = {Pith review of: System-driven Cloud Architecture Design Support with Structured State Management and Guided Decision Assistance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3XIDVSHP}},
  note         = {Machine review of arXiv:2505.20701}
}
read the original abstract

Cloud architecture design is a complex process requiring both technical expertise and architectural knowledge to develop solutions from frequently ambiguous requirements. We present CloudArchitectBuddy, a system-driven cloud architecture design support application with two key mechanisms: (1) structured state management that enhances design understanding through explicit representation of requirements and architectural decisions, and (2) guided decision assistance that facilitates design progress through proactive verification and requirement refinement. Our study with 16 industry practitioners showed that while our approach achieved comparable design quality to a chat interface, participants rated our system higher for usability and appreciated its ability to help understand architectural relationships and identify missing requirements. However, participants also expressed a need for user-initiated interactions where they could freely provide design instructions and engage in detailed discussions with LLMs. These results suggest that integrating a chat interface into our structured and guided workflow approach would create a more practical solution, balancing systematic design support with conversational flexibility for comprehensive cloud architecture development.

Figures

Figures reproduced from arXiv: 2505.20701 by the authors.

Figure 1
Figure 1. Comparison of chat UI and CloudArchitectBuddy. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. System architecture of CloudArchitectBuddy. The upper part shows structured state models, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Example transitions and structural representations of [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: System interfaces. (a), (b), and (c) show state views of the [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Our prompt template for Architecture Proposal [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: Our prompt template for Architecture Summary [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Our prompt template for Architecture Inspection [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]
Figure 8
Figure 8. Figure 8: Our prompt template for Architecture Inquiry [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]

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Reference graph

Works this paper leans on

41 extracted references · 35 canonical work pages

  1. [1]

    Cloud Design Patterns: Prescriptive Architecture Guidance for Cloud Applications

    Alex Homer, John Sharp, Larry Brader, Masashi Narumoto, and Trent Swanson. Cloud Design Patterns: Prescriptive Architecture Guidance for Cloud Applications. Microsoft patterns & practices, 2014

    work page 2014

  2. [2]

    Architectural requirements for cloud computing systems: An enterprise cloud approach

    Bhaskar Prasad Rimal, Admela Jukan, Dimitrios Katsaros, and Yves Goeleven. Architectural requirements for cloud computing systems: An enterprise cloud approach. Journal of Grid Computing, 9:3–26, 2011

    work page 2011

  3. [3]

    Devops and software quality: A systematic mapping

    Alok Mishra and Ziadoon Otaiwi. Devops and software quality: A systematic mapping. Computer Science Review, 38:100308, 2020

    work page 2020

  4. [4]

    Assessment of benefits and disadvantages of implementing cloud-specific solutions in polish companies on the example of erp systems

    Remigiusz Zieli ´nski, Sebastian Kot, and Katarzyna Zieli ´nska. Assessment of benefits and disadvantages of implementing cloud-specific solutions in polish companies on the example of erp systems. Procedia Computer Science, 225:70–79, 2023. 27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023)

    work page 2023

  5. [5]

    The use of the multi-criteria ahp method to select a cloud computing provider

    Piotr Niemcewicz. The use of the multi-criteria ahp method to select a cloud computing provider. Procedia Computer Science, 192:2558–2567, 2021. Knowledge-Based and Intelligent Information and Engineering Systems: Proceedings of the 25th International Conference (KES 2021)

    work page 2021

  6. [6]

    C., Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B

    Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Vageesh D. C., Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B. Ashok, and Shashank Shet. Codeplan: Repository-level coding using llms and planning.Proceedings of the ACM on Software Engineering, 1(FSE):675–698, 2024

    work page 2024

  7. [7]

    Using an llm to help with code understanding

    Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu, and Brad Myers. Using an llm to help with code understanding. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, pages 1–13, 2024

    2024

  8. [8]

    Jules White, Sam Hays, Quchen Fu, Jesse Spencer-Smith, and Douglas C. Schmidt. Chatgpt prompt patterns for improving code quality, refactoring, requirements elicitation, and software design. In Generative AI for Effective Software Development, pages 71–108. Springer, 2024

    work page 2024

Show all 41 references
  1. [9]

    Advancing requirements engineering through generative ai: Assessing the role of llms

    Chetan Arora, John Grundy, and Mohamed Abdelrazek. Advancing requirements engineering through generative ai: Assessing the role of llms. In Generative AI for Effective Software Development, pages 129–148. Springer, 2024

    2024

  2. [10]

    Using llms in software requirements specifica- tions: An empirical evaluation

    Madhava Krishna, Bhagesh Gaur, Arsh Verma, and Pankaj Jalote. Using llms in software requirements specifica- tions: An empirical evaluation. In 2024 IEEE 32nd International Requirements Engineering Conference, page 475–483, 2024

    2024

  3. [11]

    Supporting early architectural decision-making through tradeoff analysis: A study with volvo cars

    Karl Öqvist, Jacob Messinger, and Rebekka Wohlrab. Supporting early architectural decision-making through tradeoff analysis: A study with volvo cars. In Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, pages 411–416, 2024

    2024

  4. [12]

    Large language models for software engineering: A systematic literature review

    Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. Large language models for software engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology, 33(8):1–79, 2024

    2024

  5. [13]

    Chudziak

    Konrad Cinkusz and Jaroslaw A. Chudziak. Towards llm-augmented multiagent systems for agile software engineering. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, page 2476–2477, 2024

    2024

  6. [14]

    Sarah Fakhoury, Aaditya Naik, Georgios Sakkas, Saikat Chakraborty, and Shuvendu K. Lahiri. Llm-based test-driven interactive code generation: User study and empirical evaluation. IEEE Transactions on Software Engineering, 50(9):2254–2268, 2024

    2024

  7. [15]

    Experimenting a new programming practice with llms

    Simiao Zhang, Jiaping Wang, Guoliang Dong, Jun Sun, Yueling Zhang, and Geguang Pu. Experimenting a new programming practice with llms. arXiv preprint arXiv:2401.01062, 2024

    2024 arXiv

  8. [16]

    Elicitron: An llm agent-based simulation framework for design requirements elicitation

    Mohammadmehdi Ataei, Hyunmin Cheong, Daniele Grandi, Ye Wang, Nigel Morris, and Alexander Tessier. Elicitron: An llm agent-based simulation framework for design requirements elicitation. arXiv preprint arXiv:2404.16045, 2024

    2024 arXiv

  9. [17]

    we need structured output

    Michael Xieyang Liu, Frederick Liu, Alexander J Fiannaca, Terry Koo, Lucas Dixon, Michael Terry, and Carrie J Cai. " we need structured output": Towards user-centered constraints on large language model output. In Extended Abstracts of the CHI Conference on Human Factors in Co...

    2024

  10. [18]

    Structured information extraction from scientific text with large language models

    John Dagdelen, Alexander Dunn, Sanghoon Lee, Nicholas Walker, Andrew S Rosen, Gerbrand Ceder, Kristin A Persson, and Anubhav Jain. Structured information extraction from scientific text with large language models. Nature Communications, 15:1418, 2024

    2024

  11. [19]

    Jiaqi Li, Mengmeng Wang, Zilong Zheng, and Muhan Zhang. LooGLE: Can long-context language models understand long contexts? In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, pages 16304–16333, 2024

    2024

  12. [20]

    Towards llm-driven dialogue state tracking

    Yujie Feng, Zexin Lu, Bo Liu, Liming Zhan, and Xiao-Ming Wu. Towards llm-driven dialogue state tracking. arXiv preprint arXiv:2310.14970, 2023

    2023 arXiv

  13. [21]

    A survey on proactive dialogue systems: problems, methods, and prospects

    Yang Deng, Wenqiang Lei, Wai Lam, and Tat-Seng Chua. A survey on proactive dialogue systems: problems, methods, and prospects. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pages 6583–6591, 2023

    2023

  14. [22]

    Towards human-centered proactive conversational agents

    Yang Deng, Lizi Liao, Zhonghua Zheng, Grace Hui Yang, and Tat-Seng Chua. Towards human-centered proactive conversational agents. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, page 807–818, 2024

    2024

  15. [23]

    Hiertod: A task-oriented dialogue system driven by hierarchical goals

    Lingbo Mo, Shun Jiang, Akash Maharaj, Bernard Hishamunda, and Yunyao Li. Hiertod: A task-oriented dialogue system driven by hierarchical goals. arXiv preprint arXiv:2411.07152, 2024

    2024 arXiv

  16. [24]

    A survey on rag meeting llms: Towards retrieval-augmented large language models

    Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li. A survey on rag meeting llms: Towards retrieval-augmented large language models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page ...

    2024

  17. [25]

    We will use Cloud SQL as the d at ab as e

    Basic G u i d e l i n e s for Answers - Focus on overall a r c h i t e c t u r e design , avoid i m p l e m e n t a t i o n details - ( Good ) " We will use Cloud SQL as the d at ab as e " - ( Bad ) " Install Cloud SQL using the f o l l o w i n g c om ma nds " - Replace t e c ...

  18. [26]

    Pinned

    Service S e l e c t i o n C ri ter ia - Include s er vi ces marked as " Pinned " in user i n f o r m a t i o n as m a n d a t o r y - C on sid er user e v a l u a t i o n with the f o l l o w i n g p r i o r i t i e s : - Highest p ri or it y : Pinned se rv ic es ( m a n d a t...

  19. [27]

    adl ": A r c h i t e c t u r e diagram in Mermaid not at io n -

    Output S t r u c t u r e Please s t r u c t u r e your answer in the f o l l o w i n g format : - " adl ": A r c h i t e c t u r e diagram in Mermaid not at io n - " s ec ur it y ": S ec ur ity summary - " r e l i a b i l i t y ": R e l i a b i l i t y summary - " s c a l a b ...

  20. [28]

    A r c h i t e c t u r e Diagram ( adl ) G u i d e l i n e s - Write in Mermaid no ta tio n without code fence - Include the f o l l o w i n g e lem en ts : - System c o m p o n e n t s - Data flow - Process flow - Users and s t a k e h o l d e r s - System r e l a t i o n s h ...

  21. [29]

    Using Cloud SQL for MySQL enables rel ia bl e da ta ba se o p e r a t i o n s at low cost , en su rin g secure cu st om er data m a n a g e m e n t

    Summary Writing G u i d e l i n e s - For each aspect , explain both t e c h n i c a l i m p l e m e n t a t i o n and bu si ne ss value - ( Good ) " Using Cloud SQL for MySQL enables rel ia bl e da ta ba se o p e r a t i o n s at low cost , en su rin g secure cu st om er data...

  22. [30]

    [ Output Format ]

    Other - Answer in English [ Input Format ] ... [ Output Format ] ... Figure 6: Our prompt template for Architecture Summary 12 You are an e x c e l l e n t cloud a r c h i t e c t . Please review the pr op ose d a r c h i t e c t u r e . [ I n s t r u c t i o n s ]

  23. [31]

    Service a v a i l a b i l i t y risk due to single - region c o n f i g u r a t i o n

    Basic Policy for I d e n t i f y i n g C on cer ns - List a r c h i t e c t u r a l design co nc er ns in order of pr io rit y - Focus on s t r u c t u r a l a r c h i t e c t u r a l c on cer ns rather than o p e r a t i o n a l or i m p l e m e n t a t i o n details - ( Good...

  24. [32]

    S t r u c t u r e for D e s c r i b i n g C onc er ns Explain each concern using the f o l l o w i n g s t r u c t u r e : - Name of concern - Reason - D et ail ed e x p l a n a t i o n i n c l u d i n g b us in ess impact - Address both t e c h n i c a l c h a l l e n g e s a...

  25. [33]

    Single d at aba se c o n f i g u r a t i o n risks service i n t e r r u p t i o n and loss of sales o p p o r t u n i t i e s during f ai lu res

    Focus of E x p l a n a t i o n - Connect t e c h n i c a l co nc er ns with bu sin es s impact - ( Good ) " Single d at aba se c o n f i g u r a t i o n risks service i n t e r r u p t i o n and loss of sales o p p o r t u n i t i e s during f ai lu res " - ( Bad ) " D ata ba ...

  26. [34]

    Scope of E x p l a n a t i o n - Focus on infrastructure - level c onc er ns - Exclude c on ce rns about a p p l i c a t i o n i m p l e m e n t a t i o n details - Include p e r s p e c t i v e s on cost , security , availability , performance , and m a i n t a i n a b i l i t y

  27. [35]

    [ Output Format ]

    Other - Answer in English - When using t e c h n i c a l terms , include brief e x p l a n a t i o n s as needed - Keep e x p l a n a t i o n s for each concern concise and sp ec ifi c [ Input Format ] ... [ Output Format ] ... Figure 7: Our prompt template for Architecture In...

  28. [36]

    Basic G u i d e l i n e s for Q u e s t i o n s - Provide q u e s t i o n s that can be a ns we red with Yes / No - P r i o r i t i z e q u e s t i o n s di rec tl y related to bu si ne ss r e q u i r e m e n t s - List q u e s t i o n s in order of pri or it y - Avoid q u e s...

  29. [37]

    Would you like to ...?

    Q ue st io n Format - Phrase q u e s t i o n s as " Would you like to ...?" - Avoid q u e s t i o n s asking for n u m e r i c a l values or method s e l e c t i o n - ( Good ) Would you like to scale the system a u t o m a t i c a l l y ? - ( Bad ) How many users do you expec...

  30. [38]

    Q ue st io n Scope - Limit q u e s t i o n s to those a f f e c t i n g cloud a r c h i t e c t u r e design d e c i s i o n s - ( Good ) Would you like to provide s er vic es across m ul ti pl e regions ? - ( Bad ) Would you like to support mu lt ip le l a n g u a g e s ? - A...

  31. [39]

    R e l e v a n c e to R e q u i r e m e n t s - P r i o r i t i z e q u e s t i o n s related to pr ovi de d r e q u i r e m e n t s - Avoid q u e s t i o n s about a p p l i c a t i o n i m p l e m e n t a t i o n details - Avoid q u e s t i o n s that d u p l i c a t e e xi s...

  32. [40]

    would you like to

    Q ue st io n P e r s p e c t i v e - Ask q u e s t i o n s about user i n t e n t i o n s (" would you like to ") - Avoid q u e s t i o n s about t e c h n i c a l n e c e s s i t y (" is it n e c e s s a r y ") - Focus on infrastructure - related q u e s t i o n s - ( Good ) ...

  33. [41]

    Other - Replace t e c h n i c a l terms with simple e x p r e s s i o n s wh en ev er p os si ble - Answer in English Figure 8: Our prompt template for Architecture Inquiry 14

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.