Prompt Engineering
Prompt engineering is crafting the instruction you give a model — the wording, examples, and output format — to get better results without retraining it. It's the most visible AI skill and, as models improve, increasingly table stakes rather than a moat.
Prompt engineering is the craft of the instruction itself: how you phrase the task, what examples you include, what format you ask for, and how you structure the request. It’s the first lever everyone reaches for, and it still matters — a clearer prompt produces better output.
What’s changed is its weight. As models got better at understanding intent, clever wording stopped being a durable advantage; it’s now table stakes. The leverage moved outward to what you put around the prompt — the documents, history, and tools you supply (context engineering) and what the system remembers across tasks (memory engineering). Prompt engineering is still necessary, just no longer where the differentiation lives.
Go deeper
From the conversation
-
Agent Memory: The Last Battleground in the AI Stack | Richmond Alake, Oracle -
How Intercom Cut $250K/Month by Ditching GPT for Qwen - Architecting AI Agents: The Shift from Models to Systems | Aishwarya Srinivasan
- Explaining Eval Engineering | Galileo's Vikram Chatterji
- Vercel's Playbook for AI Agents: From Vibe Check to Production | Malte Ubl
- From Demo to Defensibility: How to Build an AI Business that Lasts | Aurimas Griciūnas
- Context Poisoning Is Killing Your AI Agents: How to Stop It
- The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn