Claude Prompt to Optimize AI Code Completion Context

What this prompt does

This prompt asks Claude to tune your repository so AI completion tools produce noticeably better suggestions. You supply the [project_type], [tech_stack], and [project_structure], and the model designs the context scaffolding that completion engines read — rules files, rich type definitions, example files, and a domain document — rather than just suggesting code inline.

The structure works because it targets every channel an assistant uses to understand your code. A .cursorrules or copilot-instructions.md file encodes [convention_count] explicit rules; type definitions and docstring templates for your [key_modules] give the model strong type context; example files for each pattern act as implicit few-shot prompts; and a project-level document explaining [domain_context] stops the AI from inventing wrong entity relationships. Barrel files and import structure help it discover related code across [module_count] modules, snippet libraries cover [common_patterns], and workspace settings cap context and exclude build artifacts. Together these shape what the tool sees, which is what shapes what it suggests.

When to use it

• Your AI completions feel generic and keep ignoring your conventions.
• You are starting a new repo and want AI-friendly structure from day one.
• You have a complex [domain_context] the assistant keeps misunderstanding.
• You want consistent suggestions across a team using the same tooling.
• Your [tech_stack] has rich types that the AI is underusing.
• You need a documented set of [convention_count] rules new contributors and AI both follow.

Example output

Expect concrete artifacts rather than abstract suggestions. You get a drafted rules file with numbered conventions ready to commit, type-definition or docstring templates for your [key_modules] that include generic, union, and branded types where they help, and skeleton example files — a model, a controller, a service, and a test — that serve as implicit few-shot patterns. Alongside those sit a domain-model document describing entities, relationships, and business rules, guidance on barrel-file and import structure to help the AI discover related code across [module_count] modules, snippet definitions with tab-stops for [common_patterns], and recommended workspace settings covering suggestion behavior, context-file limits, and ignored directories like node_modules and build output.

Pro tips

• Make [convention_count] rules specific and enforceable — "use Zod for input validation," not "write clean code."
• Invest most in the domain document; encoding [domain_context] is the step most teams skip and the one that prevents wrong-relationship hallucinations.
• Keep example files real and current, since the AI treats them as few-shot examples and will copy their style verbatim.
• List your actual [key_modules] so the type context lands where the AI most often guesses wrong.
• Re-run the prompt when your [tech_stack] or conventions change; stale rules files quietly steer the AI toward outdated patterns.
• Trim ignored directories carefully so the assistant still sees source it needs while skipping node_modules and build output.