International Journal For Multidisciplinary Research

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Overcoming Context Length Limitations in LLM’s Integrating LSTM, Retrieval-Augmented Generation, and Agentic Frameworks for Enhanced Business Data Analysis

Author(s) Dhruvansh Gandhi, Aryan Giri, Swati Uparkar
Country India
Abstract Large Language Models (LLMs) such as GPT and BERT demonstrate remarkable capabilities in various natural language processing (NLP) tasks. However, their performance is constrained by context length limitations, leading to inefficiencies in processing extended text sequences. This paper explores the challenges posed by context length limitations and proposes innovative solutions combining Long Short-Term Memory (LSTM), Retrieval-Augmented Generation (RAG), and Agentic Framework. We present an AI-powered solution tailored for businesses, enabling efficient data processing, analysis, and visualization. The solution integrates actionable insights, streamlines operations, and drives growth through automated data integration, AI-powered analytics, and interactive visualizations.
Keywords Large Language Models (LLMs), GPT, Natural Language Processing (NLP), Long Short-Term Memory (LSTM), Retrieval-Augmented Generation (RAG), Agentic Framework, AI-powered Data Analytics
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-26
Cite This Overcoming Context Length Limitations in LLM’s Integrating LSTM, Retrieval-Augmented Generation, and Agentic Frameworks for Enhanced Business Data Analysis - Dhruvansh Gandhi, Aryan Giri, Swati Uparkar - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.35767
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.35767
Short DOI https://doi.org/g829q4
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