Developing A Chatbot: A Hybrid Approach Using Deep Learning and RAG
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid growth of online shopping has underscored the need for effective customer service techniques that go beyond traditional channels such as email, mobile responses, and FAQs. In this dynamic landscape, chatbots have emerged as indispensable tools for enhancing customer satisfaction and streamlining consumer interactions. These artificial intelligence-powered chatbots are reshaping the online retail industry by providing human-like engagement. Our study introduces a hybrid approach to developing a context-aware chatbot. We combine intent recognition using deep learning models with a retrieval-based argument approach, leveraging OpenAI's Large Language Model (LLM). Specifically, we compare two intent recognition models: LSTM and BERT. Additionally, we implement a retriever system that gathers relevant supporting data from a vector database storing the vector embeddings of our products. The outcome is a dynamic and customer-centric chatbot experience that harnesses the capabilities of OpenAI's LLM. Finally, we have built an Electronic Shopping Assistant capable of answering a wide range of product-related questions based on our extensive knowledge base.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it