Semantic Literature Retrieval and Code Context Engineering for Multi-File Project Accuracy

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What is the impact of combining semantic literature retrieval (Elicit) with code-focused context engineering on the accuracy of generated code for niche domains in multi-file projects, measured by. 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. 15 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of combining semantic literature retrieval (Elicit) with code-focused context engineering on the accuracy of generated code for niche domains in multi-file projects, measured by pass@k on ReCode?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance