Aug 2026· International Journal of Electrical and Computer Engineering (IJECE)· 0 citations
TL;DR
What stands out is how compressed models running locally can still catch up in understanding classroom content, and building tutors powered by artificial intelligence becomes safer when data never leaves the device and outside services are not needed at all.
Abstract
New students often struggle when short articles clash with thick textbooks. Still, even though large language models offer some teaching support, standard online setups lack focused accuracy - sometimes making things up - and risk user data control. Here comes an idea: build a tightly tested, self- contained system that aligns learning materials automatically without needing the internet, keeping information private by design. One look at two setups shows how they handle local reasoning differently. Instead of using both encoder and decoder parts, one system skips the encoder entirely. That simpler design grabs full context through ChromaDB without shrinking the data first. Meanwhile, the older type crunches input down, losing meaning along the way. Even though it runs fast - just under a second - errors pop up often, four out of five responses drifting off course. On the flip side, the new method builds correct code nearly every time, adds clear explanations tied to lesson goals, yet takes more than fourteen seconds to reply. Slower? Yes. More accurate? Clearly. What stands out is how compressed models running locally can still catch up in understanding classroom content. Another key point emerges: building tutors powered by artificial intelligence (AI) becomes safer when data never leaves the device and outside services are not needed at all.
This paper mainly talks about how Retrieval-Augmented Generation frameworks are making document-based question-answering smarter and more reliable than traditional models, and explores different types of RAG techniques like dense retrieval, vector database optimization, generative language model integration, and hybrid search frameworks.
Manthan Meshram, Kunalgir Ridhanarthi, Darshan Khirekar· Best Journal of Innovation i...· 0 citations
Due to the surge in digital document creation, intelligent systems that can effectively retrieve accurate information from unstructured PDF content have become a necessity. Current keyword-based search techniques lack semantic meaning, while large language models (LLMs), when utilized individually, produce hallucinated results when they are not contextually aware of document content. In this paper, we introduce a novel real-time question answering system for PDF content using a lightweight approach to Retrieval-Augmented Generation (RAG). Our system combines a HuggingFace sentence transformer model for semantic embeddings with a FAISS vector similarity search for efficient retrieval, leveraging a Groq-hosted LLaMA 3.3 70B model for context-grounded answer generation. The system is built using LangChain for orchestration and Streamlit for web-based deployment, and requires no GPU, no fine-tuning, and no pre-indexed corpus. Evaluation across five complementary metrics—Exact Match, Token-level F1, BLEU, ROUGE, and semantic similarity—demonstrates strong alignment between generated and reference answers, with a peak semantic similarity of 0.8709 and a mean Token F1 of 0.4701, confirming the viability of this approach for real-world document intelligence.
Mukesh Lakshmi Sai Medikonda, Kalva Vishnu Teja, K. Greeshma et al.· International Conference Com...· 0 citations
This paper presents the design and implementation of intelligent chatbots using Retrieval-Augmented Generation architecture to derive accurate, context-based responses from domain-specific knowledge sources such as PDFs and CSV files. The proposed system integrates information retrieval mechanisms with large language models to overcome crucial limitations of traditional chatbots: hallucination, knowledge cut-off, and inability to access private organizational data. The proposed architecture uses a six-stage pipeline consisting of document ingestion, text splitting, and embedding generation with sentence transformers, semantic retrieval, and response generation via Large Language Models (LLMs). By preprocessing the PDF and CSV documents using a RecursiveCharacterTextSplitter and converting them into 384-dimensional semantic embeddings, the system works via a similarity-based retrieval of document chunks. For implementation, the LlamaIndex framework was used for data indexing and query orchestration, and HuggingFace Transformers for language modeling. Experimental validation conducted on the smart campus chatbot system demonstrated significant improvements in the accuracy, relevance and transparency of responses across education, student services, and administrative support domains compared with a standalone language model. Because of its modular design, it supports a wide range of domain-specific topics without the combinatorial costs of re-training. This is another important application for systems that require regular updates and accurate information. Overall, the proposed framework provides a simple and practical approach for easily implementing domain-aware chatbots.
Varsha Spoorthi Shree Narravula, V. K, Yashaswini B. Suresh et al.· 2026 7th International Confe...· 0 citations
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing work combines user behavior sequences with large language models (LLMs) to model user preferences. In practice, feature engineering remains critical to model effectiveness, yet its complexity slows offline iteration and makes online deployment heavy and hard to reuse, all under tight online latency budgets. The root cause is a tight coupling between feature-processing logic and model architecture, where every feature change touches the training and serving code and resists reuse across scenarios. To break this coupling, we present Prompt Generation (PG), a high-level tokenizer and configuration-driven framework that decouples feature-processing logic from model architecture through two declarative JSON files, which serve as the single source of truth for both offline training and online serving, ensuring feature consistency across the two stages. Organizing features under four types with three composable processing components to assemble and compress heterogeneous features, PG delivers acceleration at three levels: (1)fast training iteration: feature experiments require only configuration changes, with built-in token compression for ultra-long sequences; (2)fast deployment: a new scenario only needs to conform to the PG schema and plug into a universal pipeline, with no scenario-specific engineering; (3)fast online inference: engine applies unified optimizations over the standardized configuration, reducing PG's overhead to a negligible level. PG has been deployed on Taobao Search with statistically significant online A/B uplifts of +0.47% in transaction count and +0.51% in GMV, and has been applied across multiple Taobao search and recommendation teams as the iteration framework for generative retrieval.
The system offers a replicable instantiation of RAG for transparent, citation-backed campus information services in Indonesian and offers a replicable instantiation of RAG for transparent, citation-backed campus information services in Indonesian.
Haddad Alwi Yafie, A. Zailani, Widang Muttaqin et al.· SinkrOn· 0 citations
Results suggest that code-mixing-aware RAG chatbots can meaningfully improve DSA learning outcomes while remaining feasible for instructor-led deployment in low-resource settings.
Saroj Bhandari, Puja Dhital· Aadim Journal of Multidiscip...· 0 citations
