Large language models in advertising: Unlocking potential and understanding boundaries

Thomas, Arun (2025) Large language models in advertising: Unlocking potential and understanding boundaries. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 1249-1258. ISSN 2582-8266

Large Language Models (LLMs) are revolutionizing the advertising industry by introducing unprecedented capabilities that transform creative processes, audience targeting, and campaign management. This technical review explores how these sophisticated neural network architectures integrate into advertising ecosystems while examining both their transformative potential and implementation challenges. LLMs demonstrate exceptional abilities in generating diverse advertising content, expanding semantic keyword understanding, modeling user intent with nuance, and automating campaign workflows. However, several obstacles hinder their seamless integration, including latency constraints incompatible with real-time bidding environments, inconsistent outputs that complicate brand voice maintenance, prompt instability requiring continuous refinement, and factual accuracy concerns that present compliance risks. The review proposes technical solutions such as multi-layered guardrail architectures, hybrid system designs that strategically deploy LLMs alongside deterministic systems, specialized evaluation frameworks beyond standard metrics, and ethical implementation guidelines. Future directions include advertising-focused model development, real-time optimization capabilities, multimodal applications spanning text and visual content, and comprehensive governance frameworks that balance innovation with responsible deployment considerations.