AutoContext: An Adaptive Framework for Intelligent Context Management in Large Language Models

Large Language Models (LLMs) have transformed the natural language processing domain by demonstrating incredible competence in reasoning, summarization, and conversation response generation. Their capacity to generate contextually well-developed and human-like responses has made them priceless in a variety of real-world use cases like customer service, learning, and content generation. Nonetheless, the performance of LLMs is limited by bounded context windows, token inefficiencies in their usage, and lack of adaptive memory components for the maintenance of long-term conversational consistency. The limitations tend to introduce token redundancy, hallucinations, and inference computational wastage. In order to step out of this hurdle, this work introduces AutoContext: a dynamic context-management mechanism that filters, summarizes, and prioritizes important information from the dialogue history in a smart way and then passes it on to the LLM. The design integrates a Random Forest–based model of context relevance to rank message salience and an abstractive summarizer from T5 to cut through lengthy blocks of conversation without sacrificing semantic coherence. The unified process supports dynamic refactoring of prompts dynamically such that the most context-relevant content is left and entered into the model. AutoContext provides a model-agnostic, scalable solution towards making LLM-based systems more efficient as well as more knowledgeable about contexts. By bridging the memory optimization vs semantic preservation gap, it paves the way for designing intelligent, cost-effective, and context-aware conversational AI systems suitable for deployment in real-world scenarios.