AI loses context — fix large-codebase memory loss in Cursor, Claude Code, and v0

Cursor’s attention is precise when you scope it and smeared when you do not. Never use @codebase for a repository larger than a few thousand lines — it pulls too many files, the working memory overflows, and the answer drifts. Instead use @-references to name the exact files: @src/lib/auth.ts @src/hooks/use-user.ts. Five files is the practical ceiling for a single task.

Split the work correctly between Cursor’s two modes. Composer is for implementation — it edits files directly and keeps attention on the diff. Ask is for design — it reasons without writing, which is what you want when the task is still ambiguous. Mixing them (“design and implement this in Composer”) blows context on the design phase and leaves nothing for implementation.

Maintain a .cursorrules file at repo root. Architecture, key types, naming conventions, files that are load-bearing. Cursor injects it into every prompt; your chat transcript no longer has to carry the same context by hand.

Claude Code reads CLAUDE.md at repo root automatically, plus any CLAUDE.md inside submodules along the current path. That cascading pattern is the highest-leverage feature in the tool — use it. Feature-level memory files are cheaper than bloating the root file, and they scope architectural notes to the part of the tree that needs them.

Use /compact at natural breakpoints. After finishing a feature, after a long debug session, after a cross-file refactor. /compact summarizes the transcript into a short synopsis and discards the rest, resetting effective working memory without losing the decisions. Without compacting, a long Claude Code session ends up in the attention-decay zone quickly.

Prefer small focused PRs over long sessions. The model that handles one feature cleanly is the same model that degrades on the tenth. If the work truly is large, scope it across sessions, not within one. Use --add-dir to let Claude Code reach files outside the current working directory without forcing the whole monorepo into context.

v0 is a front-end-only tool. Context loss in v0 almost always means you have hit its project-size ceiling — it was built to generate single components and short flows, not to carry a multi-page app in working memory. When the chat starts contradicting earlier components, rewriting a design system per turn, or losing track of shared types, the signal is: time to eject.

The migration path is v0 → Next.js. Export the generated code, move it into a real repository, and handle the app-level concerns (routing, auth, database) in your IDE with Cursor or Claude Code. The v0 migrate-out guide walks through the full handoff — folder structure, component boundaries, and the Next.js App Router idioms v0 does not generate by default.

Lovable retains chat context per project, which helps until it doesn’t. The failure mode is global prompts that touch the whole app (“refactor our auth”) — Lovable tries to hold the entire project in working memory and drifts. Scope every prompt to a single component with “Edit this component.” The model attends to the current file and ships smaller, safer edits.

Lovable also lets you edit the project description, which functions as a memory file. Make it rich. Stack, architecture, load-bearing components, naming conventions. Lovable re-reads the description when you start a new chat, which is your cheapest insurance against a fresh session losing track of design decisions.

The pattern is the same across every AI coding tool: maintain a plain-text file the AI re-reads every session. Different tools pick different filenames — CLAUDE.md, .cursorrules, AGENTS.md, README.md for tools that respect it — but the content is identical. Architecture decisions, key types, files that are load-bearing, naming conventions, explicit do-not- modify lists.

Keep the file short. Under 200 lines is the right order of magnitude. If it grows past that, split into feature-level memory files (src/auth/CLAUDE.md, src/payments/CLAUDE.md) so the AI loads only the section it needs. A 2,000-line memory file bloats every prompt and defeats the point. A 150-line memory file is the floor the AI reasons from on every turn.

Treat the memory file like a test: it earns its place by preventing a specific class of regression. Every time you catch the AI re-hallucinating, update the file. Every time the AI proposes a pattern you do not want, add a do-not rule. The file evolves into the shortest possible briefing that keeps the AI on the path.

When a session runs long, working memory degrades. The fix is a checkpoint: write a 200-word summary of decisions plus open issues, paste it into a fresh chat, and continue from there. Treat chats as disposable; state lives in files, not in transcripts.

A good checkpoint has three sections: (1) decisions — what you chose and why, in bullets; (2) in-progress — the file you were editing, the function, the failing test; (3) next step — one sentence describing the immediate next action. Paste this into a new chat along with the 3-5 files the task touches, and working memory resets with full attention on the current problem.

Claude Code’s /compact automates most of this inside one session. Cursor and Lovable do not have an equivalent yet; you write the checkpoint by hand. Either way, the discipline is the same: short chats, long files.

• The AI rewrites the same function every session, even though it is already well-implemented in the codebase
• Team members cannot onboard to the project because the AI contradicts the docs and nobody knows which is current
• New features take longer to ship than old features did — the AI velocity curve is going the wrong way
• Your CLAUDE.md is longer than your README.md — a signal the memory file is doing work that belongs in real documentation or real abstractions
• Every deployment surfaces a new regression in a file the AI “helpfully” cleaned up during an unrelated task