What is the impact of large input context support in Code Llama on code completion accuracy for multi-file pro

Abstract

Abstract: Large Language Models (LLMs) have shown promise in automating code generation and software engineering tasks, yet they often struggle with complex, multi-file projects due to context limitations and knowledge gaps. We propose a novel context engineering workflow that combines multiple AI components: an Intent Translator (GPT-5) for clarifying user requirements, an Elicit-powered semantic literature retrieval for injecting domain knowledge, NotebookLM-based document synthesis for contextual understanding, and a Claude Code multi-agent system for code generation and validation. Our integrated ap

Research Question

What is the impact of large input context support in Code Llama on code completion accuracy for multi-file projects when evaluated using the ReCode benchmark?

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