The destination is not a new LLM. It is an assistant that remembers you and gets better at being yours. Here is the path from prototype to something usable — written as gates, not milestones. Each phase has one number you have to hit before you earn the next.
The model does the talking and the reasoning, and it never changes. That capability is rented — it improves when someone else ships a better one, and swapping it is a config change.
Everything that makes the assistant yours lives outside it: a memory that consolidates, forgets the noise, indexes by structure, weights what matters, and is shaped by your feedback. Each person's copy drifts into their own.
Two paths, one store. The top runs per message. The bottom runs after, and offline. Only one box in this diagram is bought in — and it is the one everyone else is competing on.
If the frozen box gets better, you inherit it for free. If the sage boxes get better, nobody can copy it, because it is shaped by one person's history.
A milestone is something you finish. A gate is a number you either hit or you don't, and missing it means you change direction rather than push harder.
The distinction matters for a solo project, because the failure mode is not running out of ideas. It is spending six months building an application on top of a premise that was wrong in week three, and never having run the experiment that would have said so.
One number per phase. The right-hand column is the only thing that decides whether you move on.
Rough, and deliberately front-loaded. The two cheapest phases are the two that decide everything; the expensive ones only start once the premise has survived.
Three and a half weeks buys the answer to the only question that matters. P5 is drawn dashed because it is not a phase — it is a tax you start paying at P2 and never stop.
Do not skip this to go and build the app. Flat RAG is strong, simple, and well understood. If structure-indexed memory cannot beat it, you want to find out in week three, not month six.
The experiment is small on purpose. One corpus, two retrievers, the same frozen model answering the same questions, two numbers.
Both outcomes are wins in week three. The only losing move is not running it and finding out in month six.
Accuracy alone is easy to buy — retrieve more and you will usually score better. Holding the token budget equal or lower is what makes the claim about structure rather than about spending more context on the same problem.
The gap is not the engine. It is integration and evidence — which is a much better problem to have, and a much easier one to underestimate.
Naming these early is not pessimism. Each one maps to a gate, and a wall you have written down is a wall you can measure yourself against instead of walking into.
The engine is mostly built. Phase 1 tells you whether the core bet is real, and it costs three weeks. Phase 2 turns it into something a person can actually use. Everything after that is a scaling problem, and scaling problems are the good kind — they only exist if the thing works.
Don't start at the code. Start at the gate.