Generative AI Development: A Practical Framework for Building, Evaluating and Deploying LLM-Based Applications

DOI

No DOI

Published In

Volume 5, Issue 3 (2026)

Published Online

September-December 2026

Pages

752-755

Article Type

Original Article

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Generative AI has moved from research prototypes to everyday software, and building applications on large language models (LLMs) has become an engineering discipline of its own. Developers must choose between prompting, retrieval-augmented generation (RAG), fine-tuning and agentic tool use, and must then evaluate, secure, serve and monitor non-deterministic systems whose behaviour cannot be fully verified by conventional testing. This paper presents a practical framework for generative AI development. It organizes the work into an eight-stage lifecycle, describes a layered reference architecture, compares the main adaptation strategies, and sets out an evaluation methodology that combines rule-based checks, retrieval metrics, LLM-as-judge scoring and human review. It also discusses cost, latency, safety and privacy considerations and identifies open challenges. The framework is intended for students and practitioners who build LLM-based applications and is grounded in the published literature rather than new experimental results.