I've been running AI as a de facto team member in my business for the past 6 months — not the "ask ChatGPT a question" variety, but with actual job descriptions, SOPs, onboarding docs, and defined deliverables.
The core insight is boring: most AI underperformance is a management problem, not a technology problem. When you give an LLM the same structured context you'd give a new hire (role definition, brand docs, quality checklists, feedback loops), output quality and consistency improve significantly.
The playbook covers:
- Writing an AI job description (what it owns, what it doesn't)
- Onboarding documents and context management
- SOPs for recurring workflows
- Quality control and feedback loops
- When to use AI vs. when you still need a human
Would love feedback from anyone who's done something similar — curious how others are handling context persistence across sessions and quality degradation over time.
Hey HN,
I've been running AI as a de facto team member in my business for the past 6 months — not the "ask ChatGPT a question" variety, but with actual job descriptions, SOPs, onboarding docs, and defined deliverables.
The core insight is boring: most AI underperformance is a management problem, not a technology problem. When you give an LLM the same structured context you'd give a new hire (role definition, brand docs, quality checklists, feedback loops), output quality and consistency improve significantly.
The playbook covers: - Writing an AI job description (what it owns, what it doesn't) - Onboarding documents and context management - SOPs for recurring workflows - Quality control and feedback loops - When to use AI vs. when you still need a human
Would love feedback from anyone who's done something similar — curious how others are handling context persistence across sessions and quality degradation over time.