The integration of large language models (LLMs) into software engineering workflows has substantially accelerated automation across code generation, debugging, refactoring, automated test creation, and documentation tasks. Controlled evaluations and industry reports document productivity gains ranging from 20–55% in repetitive coding activities. However, persistent challenges of hallucinations, output inconsistency, and high token-based inference costs continue to limit reliable enterprise-scale adoption. These barriers originate predominantly from unstructured, ungoverned, and low-quality inference-time context rather than from model architecture or pre-training data limitations alone. Prior scholarship has devoted extensive effort to architectural innovations, fine-tuning strategies, and training corpus curation, yet systematic data engineering interventions applied to context ingestion, transformation, validation, compression, and delivery remain comparatively underexplored. This paper advances a comprehensive data engineering–centric framework that treats inference-time context as a governed, high-quality data product subject to the full lifecycle of ingestion, semantic transformation, multi-dimensional quality validation, compression, relevance ranking, hybrid retrieval, and closed-loop observability. Through rigorous synthesis of peer-reviewed literature including hallucination mitigation studies, retrieval-augmented generation (RAG) research, software engineering productivity analyses, and foundational data quality frameworks the work establishes that principled context engineering constitutes a primary determinant of LLM reliability, operational efficiency, and cost-effectiveness in software development environments. A reference architecture grounded in modern data engineering principles is presented, accompanied by a formal Context Quality Score (CQS) model with an illustrative weighted computation. Quantitative comparisons drawn from the synthesized literature demonstrate that engineered context pipelines can reduce hallucination rates by up to 40–60% in knowledge-intensive tasks, lower input token consumption by 30–50%, and decrease downstream validation overhead by 25–45%. These gains are achieved without altering underlying model parameters. The findings position context engineering as a first-class discipline within data engineering and LLM operations (LLMOps). Organizations that operationalize inference-time context as a governed data product achieve scalable, cost-efficient, and trustworthy AI-augmented software development. Implications for research and practice, together with directions for future empirical validation, are discussed in detail.

Data Engineering Context Pipelines Large Language Models LLMOps Retrieval-Augmented Generation Hallucination Mitigation Software Engineering Productivity Data Quality Context Quality Score