Leadership Co-Processing with LLMs (www.theengineeringmanager.com)

A CTO’s playbook for using large language models as “co-processors” for leadership: the author describes practical patterns—simple prompting, pair prompting (human + LLM), deep research, contrarian probing, using LLMs as executive assistants and coaches—to accelerate decision-making, document thinking, and reduce cognitive overload. The claim is that LLMs aren’t perfect but reliably add momentum, surface alternative options, and encourage deeper, slower reasoning (helping avoid snap judgments and Dunning‑Kruger blind spots). The approach shifts management work: decisions, code review, hiring and weekly communications become more collaborative, auditable, and research-driven when the LLM’s session history is preserved. Key technical takeaways and implications: keep an LLM visible and use high-quality prompts—“you get what you put in”—so complex prompts yield richer outputs; exploit real-time internet search and long context windows to compile up‑to‑date, traceable research. Pair prompting can be synchronous or asynchronous; sharing the LLM session captures the entire thought process, not just deliverables. The author provides concrete templates (e.g., a Slack-thread analyzer that summarizes issues, root causes, proposed solutions, and CTO recommendations, and a session facilitator for architecting chat apps). Caveats include model fallibility and the need for careful prompt design and human judgment. Overall, the pattern promises higher decision velocity, better documentation, and more rigorous exploration of alternatives for engineering leaders.