Explainable GENAI Techniques to Interpret and Visualize LLM Reasoning

The rapid evolution of generative artificial intelligence has redefined how machines process, reason, and communicate knowledge. Yet, the opaqueness of large language models (LLMs) continues to challenge their trustworthiness and interpretability in critical domains. This research introduces a comprehensive framework for explainable generative AI (GenAI) that seeks to decode and visualize the internal reasoning pathways of LLMs. The study integrates cognitive-inspired interpretability mechanisms with retrieval-augmented generation and semantic attribution mapping to uncover how contextual evidence shapes model responses. A novel visualization engine is developed to translate these latent reasoning traces into human-understandable graphical narratives, offering transparency across token-level, layer-level, and decision-level dimensions. Through systematic evaluation on benchmark reasoning datasets and domain-specific case studies, the proposed techniques demonstrate measurable improvements in faithfulness, causal coherence, and user interpretability. Beyond algorithmic transparency, the work also explores the epistemic implications of machine reasoning — bridging human cognitive interpretability and computational inference. The research ultimately positions explainable GenAI as a step toward ethically aligned, auditable, and cognitively comprehensible artificial reasoning systems capable of fostering accountability in next-generation intelligent technologies.