AI PAIR PROGRAMMING CONTEXT
Give Your AI Complete Codebase Context
Copilot sees one file. ChatGPT guesses dependencies. Claude hallucinates callers. LOOM gives your AI complete codebase understanding—every function, every relationship, every impact.
The Context Problem
You ask your AI assistant to help refactor a function. It looks at the function, suggests changes, and congratulates itself.
But it doesn't know how many places call that function. It doesn't know about the implicit contract with the AuthService. It doesn't know that some callers pass null and would break with the "improved" code.
AI without context is dangerous. LOOM provides the context.
Before and After: The Difference Context Makes
When AI knows what LOOM knows, everything changes.
AI Without LOOM
- Sees only the current file
- No dependency awareness
- Suggests breaking changes
- Generic boilerplate responses
- Misses architectural patterns
AI With LOOM
- Full codebase awareness
- Knows every caller and dependency
- Respects existing contracts
- Project-specific suggestions
- Follows your architectural patterns
How LOOM Powers Your AI
Complete Context Injection
When you ask a question, LOOM provides the AI with complete information: what the function does, who calls it, what it depends on, what would break if it changed.
Pattern Recognition
LOOM shows your AI how your codebase actually works. Not generic patterns from the internet—your patterns. Your naming conventions. Your architectural decisions.
Impact Analysis
Before the AI suggests a change, LOOM shows what would break. The AI can adjust its suggestion to avoid cascading failures.
Documentation Generation
Ask your AI to document your code—and watch it actually understand what it's documenting. LOOM provides the relationships, the AI provides the words.
What You Can Do
Safe Refactoring
"Refactor this function" becomes "Refactor this function while maintaining compatibility with its 23 callers."
Architecture-Aware Generation
"Add a new endpoint" becomes "Add a new endpoint following your Repository/Service/Controller pattern."
Intelligent Code Review
AI review that understands impact. Shows when changes affect critical paths with many downstream dependencies.
Accurate Documentation
Generate docs that are actually right. The AI knows what calls what, so descriptions are accurate.
Test Generation
Generate tests that cover real edge cases. LOOM shows how the function is actually used.
Bug Investigation
"Why might this be null?" The AI can trace back through callers to find the actual source.
Works With Your Tools
LOOM provides context. Use it with whatever AI you prefer.
Copilot
ChatGPT
Claude
Cursor
THE FUTURE OF AI PROGRAMMING
AI-Assisted Development Done Right
AI pair programming is revolutionizing software development. But the current generation of AI coding tools—Copilot, ChatGPT, Claude, Cursor—share a fatal flaw: they operate without seeing your actual codebase architecture.
The result? Confident suggestions that break production. "Improvements" that ignore 50 downstream callers. Refactoring advice that creates new bugs instead of fixing old ones.
LOOM bridges the gap between AI capability and codebase reality. Export your architecture. Feed it to your AI. Get suggestions that actually work.
How LOOM Works With Your AI Tools
Same codebase intelligence, tailored to each tool's strengths.
GitHub Copilot Integration
Copilot excels at code completion but operates file-by-file. It doesn't know what's in your other modules.
With LOOM: Export the dependency profile of the function you're editing. Include it in your Copilot Chat. Now Copilot knows that processOrder() is called from 23 places, three of which pass null for the optional parameter. Its suggestions respect existing contracts.
Best for: Code completion, inline suggestions, quick refactors where context prevents breaking changes.
Claude & ChatGPT Integration
Claude and ChatGPT have large context windows—they can digest substantial architectural information. But you have to give it to them.
With LOOM: Export your module's call graph as structured data. Paste it into the conversation. Claude now understands not just your current function, but how it fits into the system. Ask for a refactor, and it accounts for downstream impact. Ask for a new feature, and it follows your existing patterns.
Best for: Architectural discussions, complex refactoring, generating code that fits existing patterns, documentation.
Cursor & Windsurf Integration
Cursor and Windsurf are built around AI-first coding workflows. They already understand they need context—but they can only see what's in your project files.
With LOOM: Generate a codebase summary: key architectural patterns, critical paths, high-impact functions. Include it as a persistent context file. Now every Cursor session starts with architectural awareness. Every suggestion respects your system's structure.
Best for: Full project context, ongoing development sessions, maintaining consistency across long coding sessions.
Custom LLM Pipelines
Building your own AI-assisted development tools? LOOM exports structured data that any LLM can consume.
With LOOM: Query the registry API for any element's dependency profile. Get JSON with callers, callees, complexity scores, and file locations. Embed this in your custom prompts. Build retrieval-augmented generation (RAG) systems that pull relevant architectural context automatically.
Best for: Custom tooling, CI/CD integration, automated code review, enterprise-scale AI workflows.
The Context-Aware Workflow
Three steps that transform AI suggestions from generic to architecture-aware.
1. Query
Before you prompt, query LOOM for the element you're working with. Get its full dependency profile: who calls it, what it calls, what patterns exist nearby.
2. Include
Add the context to your AI prompt. "This function is called from 47 places. Here are the three most critical callers. Here's how similar functions handle this pattern."
3. Verify
Before accepting AI output, check it against the dependency graph. Does the suggestion break callers? Does it duplicate existing functionality? Does it follow your patterns?
This workflow is what separates "AI that helps" from "AI that creates tech debt." Context makes the difference.
Give Your AI the Context It Deserves
Stop getting generic suggestions. Start getting code that actually understands your codebase. The difference between AI that helps and AI that breaks things is context.
Free tier includes AI context features. No credit card required. Works with any AI tool.
Our AI Development Methodology
Learn the frameworks behind effective AI-assisted development.