Prompt engineering is the work of phrasing a single instruction well. You sit in front of the model, find the right wording, maybe add an example or two, and get a better answer than you would have with a lazy request. It is a real skill and it is also a small one, because it operates on one turn at a time. The moment the session ends, or the project grows past what fits in a single exchange, the careful phrasing stops mattering. You are back to a blank model that knows nothing about what you are building.

Context engineering is the work of building everything the model sits inside. Most of the writing on the term is about retrieval pipelines and agent memory and how to pack a context window, which is fine but not what I want to talk about. I mean the unglamorous human version: the documents, schemas, logs, and guardrails you maintain by hand so that a stateless model can do useful work on a real codebase over weeks. The prompt is what you say. The context is everything that is true when you say it. If you only invest in the first, you get good single answers and an incoherent project. If you invest in the second, the individual prompts barely matter, because the model is working inside something that already makes sense.

The first part of that is deciding the shape of the thing before you let the model generate any of it. I write the schema and the API rules first, before any feature work. Not because the model needs them, though it does, but because writing them is how I find out whether I actually understand what I am building. A vague idea survives in your head indefinitely. It does not survive being written down as a concrete data model with concrete endpoints. If I cannot specify the contract, I do not understand the feature yet, and no amount of clever prompting will rescue a thing I have not understood. Once the contract exists, the model can generate against it, and so can the next session, and the two will agree because they are both working from the same fixed reference rather than from two slightly different guesses.

The harder part is that the model has no memory, and you have to supply it yourself. Every new session starts blind. It does not know what you decided yesterday or why. So the context you carry between sessions has to live in files, not in your head and not in a chat history that gets truncated. Two documents do most of that work for me. The first is a handoff document that says where the project currently stands, what is done, what is in progress, and what the next session should pick up, so a fresh session can read it and continue instead of relearning the whole codebase from scratch. The second is an append-only decision log. Every time I take a shortcut because of some constraint at the time, or pick one approach over another for a reason that is not obvious from the code, it goes in the log and never gets edited out. The value is in the append-only part. Six weeks later, when something looks wrong, the log tells me whether it was a mistake or a deliberate trade I made for a reason I have since forgotten. Without it, every old decision becomes a small mystery, and you lose time re-arguing things you already settled.

What this setup actually buys you is fewer hallucinations, and it is worth being precise about why. The model does not invent things because you phrased the prompt badly. It invents things to fill gaps it cannot see across. When the schema is undefined and the past decisions are undocumented, those gaps are everywhere, and the model papers over them with plausible guesses. When the contract is fixed and the decisions are written down, there is far less room to guess, because the answer is already on the page. Good context makes the model more accurate by removing the empty space the inaccuracy comes from.

The last piece is making sure unfinished or risky work cannot slip by unnoticed, which matters more as the model writes more of the code. Anything on the frontend that is faked, stubbed, or temporary gets a hardcoded warning sitting right there in the interface, and the warning only comes out when the underlying thing is actually finished. That way a placeholder cannot quietly graduate into something that looks done. New features have to pass backtests against the existing ones, so that adding something cannot silently break what already worked. And there is a hard line that some parts are not the model’s to finish. Anything touching security, or anything with legal exposure, gets reviewed and tested by a human every time, regardless of how confident the generated version looks.

That last rule is the one I would not bend. The useful division of labor is not a percentage of how much code the model writes against how much I write, though the model does write most of it. It is a division by risk. The model handles the high-volume, well-specified work where a mistake is cheap and visible. Humans keep the parts where a mistake is expensive or hard to undo. The human stays in the loop. The aim is to spend their attention on the places that need it, rather than spreading it thin over code the model could have written correctly on its own.

None of this is worth doing for a script you will run once and delete. The overhead only pays off on something you have to keep, extend, and trust later. But for anything in that category, the wording of any single prompt turns out to be one of the least important decisions you make. What matters is whether the model is working inside a structure that holds its shape after you close the laptop. Prompt engineering gets you a good answer now. Context engineering is what makes the answer still fit a month later.