@akshay_pachaar: from prompt to context to harness engineering. three terms keep coming up in AI engineering, and they get conflated all…

from prompt to context to harness engineering. three terms keep coming up in AI engineering, and they get conflated all the time. here is the cleanest way to understand what each one is and how they fit together. 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲. the model has no memory of anything before this single call, so the prompt has to carry the full universe of what it needs to know. that means a role, some background, the instructions, a few examples, and a format. these get assembled into one input and sent to the model. when the output falls short, the skill is figuring out which ingredient is actually letting you down, not rewriting the instructions every time. the unit of work is one input. 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗲𝗺𝗼𝗿𝘆. across multiple steps, the window is finite and the information available is not, which forces a curation step. without it, important details get buried under stale tool outputs and old turns, and the model's attention degrades on the things that actually matter. a curator selects what stays, compresses what is useful but bulky, and drops the rest. each step's output then feeds into the next step, where good curation is more about knowing what to throw away than packing more in. the unit of work is what stays in the window, step by step. 𝗵𝗮𝗿𝗻𝗲𝘀𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗮𝗰𝗵𝗶𝗻𝗲. on its own, a model just generates text. the harness is what turns it into something that can take actions, check its own work, and recover when a step goes wrong. the full loop has three phases: - 𝗴𝗮𝘁𝗵𝗲𝗿 pulls together everything the model needs - 𝗮𝗰𝘁 runs the model and calls tools or sub-agents - and 𝘃𝗲𝗿𝗶𝗳𝘆 checks the output with tests or a judge on failure, the whole loop retries with updated context, which is the entire difference between calling an API and running an agent. the unit of work is the machine itself. here is the part that ties it together. prompt engineering and context engineering both live inside 𝗴𝗮𝘁𝗵𝗲𝗿. the harness is the outer container, context is what it curates, and the prompt is what it finally hands to the model. zoom out and the unit of work gets bigger. zoom in and you are back at the prompt. i also published this deep dive (article) on agent harness engineering, covering the orchestration loop, tools, memory, context management, and everything else that transforms a stateless LLM into a capable agent. the article is quoted below.