This paper presents Ibtcode, a symbolic language designed to enhance emotion-aware and intent-driven artificial intelligence systems. Traditional large language models (LLMs) often generate generic and non-actionable responses, especially in customer support scenarios. To address this limitation, we propose a hybrid architecture combining symbolic reasoning (Ibtcode) with retrieval-augmented generation (RAG) and controlled prompt engineering. Ibtcode encodes user intent, emotion, context, and expectation into a structured symbolic format. The system is evaluated using real-world customer support email queries and compared with a baseline LLM. Results show significant improvements in relevance, emotional alignment, and actionability. This work demonstrates the effectiveness of combining symbolic reasoning with neural models to build more reliable and human-aligned AI systems.
ibtesham akhtar (Sun,) studied this question.