Automated Context Generation for AI Code Assistants: An LLM-Powered Framework for Developer Intent Capture and Documentation Automation
Author(s): Althaf Khan Pattan
Publication #: 2606010
Date of Publication: 13.05.2026
Country: United States
Pages: 1-10
Published In: Volume 12 Issue 3 May-2026
DOI: https://doi.org/10.62970/IJIRCT.v12.i3.2606010
Abstract
AI coding assistants have changed how developers write and maintain software, but these tools face a persistent constraint: they can parse source code syntax yet lack access to the business logic, design rationale, and domain knowledge that shaped it. This paper presents contextify-ai, a framework that addresses this context gap through automated generation of colocated .context.md files triggered at git commit time. The framework introduces a dual-section file format where a prose section documents purpose, business rules, and decision rationale for human readers, while a YAML section encodes component interfaces, state management, dependency graphs, and render conditions for direct consumption by AI tools. A smart-diff algorithm built on AST-based structural hashing distinguishes meaningful code changes from cosmetic edits, reducing unnecessary LLM API calls by an estimated 50-70%. A developer-in-the-loop verification system captures stated intent at commit time and cross-references it against detected code changes to catch mismatches before the commit proceeds. The framework supports a provider-agnostic LLM abstraction covering commercial APIs, free-tier services, and local model hosting. Simulated evaluations across projects of varying scale show that the framework maintains documentation freshness, cuts onboarding friction, and improves AI tool accuracy in code generation tasks.
Keywords: AI-assisted development, context generation, documentation automation, large language models, developer tools, AST analysis, git hooks.
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