Using AI in Software Design: How ChatGPT Can Help With Creating a Solution Arch.
summary
Walks through solving the iSAQB Advanced example "Big Spender" by priming GPT‑4 paid model with PDFs and images, using embeddings plus a vector DB to fetch chunks, the code interpreter for testable iterations and PlantUML to generate diagrams. Notes token limits (8k frontend, up to 128k token context via API), finds ChatGPT asks ~10 clarifying questions, produces ASCIIDo documentation and diagrams that are useful but require human verification and would not pass the exam unchanged.
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chapters (15)
- 0:00AI in software design
- 1:49iSAQB and exam task
- 3:44How LLMs like ChatGPT work
- 8:53Context and memory in ChatGPT
- 15:28Embeddings and custom data
- 18:42Prompt engineering
- 25:57Preparing the model with documents and glossary
- 26:20Solving Big Spender task
- 29:17Quality attributes and scenarios
- 31:48Utility trees and diagrams
- 33:14Solution strategy
- 37:29Technical context diagram
- 40:24Business to tech mapping
- 42:53Generating quality scenarios
- 43:56Conclusion
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