Uploaded December 2025 | Updated September 2026, 2 weeks ago
Learn a concrete, repeatable way to turn fleeting ideas into portable, actionable artifacts using LLM-driven workflows. This video walks through my “ideation zero” pipeline: a YAML-driven sequence of prompt steps (capture the impulse → why-ladder → divergence → report), structured prompt files, and an MD template that becomes the canonical summary stored on disk. We discuss why this approach beats relying on compressed model memory, how most prompts are LLM-generated then hand-tuned, and why the resulting reports make your work portable across models (Claude → Gemini → Codex) and tools (CLI, apps).
If you want the example YAML + prompt templates, get them on our Slack Channel.
Join our Slack channel: aisc-to.slack.com
#LLMWorkflows #PromptEngineering #YAML #AIAutomation #Productivity #MemorySystem #RAG #AI #DeveloperTools #MachineLearning
Learn a concrete, repeatable way to turn fleeting ideas into portable, actionable artifacts using LLM-driven workflows. This video walks through my “ideation zero” pipeline: a YAML-driven sequence of prompt steps (capture the impulse → why-ladder → divergence → report), structured prompt files, and an MD template that becomes the canonical summary stored on disk. We discuss why this approach beats relying on compressed model memory, how most prompts are LLM-generated then hand-tuned, and why the resulting reports make your work portable across models (Claude → Gemini → Codex) and tools (CLI, apps).
If you want the example YAML + prompt templates, get them on our Slack Channel.
Join our Slack channel: aisc-to.slack.com
#LLMWorkflows #PromptEngineering #YAML #AIAutomation #Productivity #MemorySystem #RAG #AI #DeveloperTools #MachineLearning
