# AMGPT: a Large Language Model for Contextual Querying in Additive Manufacturing

arXiv: https://arxiv.org/abs/2406.00031 · HTML: https://mlsanity.com/paper/2406.00031/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 24 May 2024.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [Predicting Defects in Laser Powder Bed Fusion using in-situ Thermal Imaging Data and Machine Learning](https://mlsanity.com/paper/2112.11212/) (2021)

## Built on by (most cited first, 23 of 23)

- [LLM-3D Print: Large Language Models To Monitor and Control 3D Printing](https://mlsanity.com/paper/2408.14307/) (2024)
- [Automating MD simulations for Proteins using Large language Models: NAMD-Agent](https://mlsanity.com/paper/2507.07887/) (2025)
- [NANOGPT: A Query-Driven Large Language Model Retrieval-Augmented Generation System for Nanotechnology Research](https://mlsanity.com/paper/2502.20541/) (2025)
- [AdditiveLLM: Large Language Models Predict Defects in Additive Manufacturing](https://mlsanity.com/paper/2501.17784/) (2025)
- [Large Language Models for Extrapolative Modeling of Manufacturing Processes](https://mlsanity.com/paper/2502.12185/) (2025)
- [Agentic Additive Manufacturing Alloy Evaluation](https://mlsanity.com/paper/2510.02567/) (2025)
- [Towards an automated workflow in materials science for combining multi-modal simulative and experimental information using data mining and large language models](https://mlsanity.com/paper/2502.14904/) (2025)
- [Multimodal RAG-driven Anomaly Detection and Classification in Laser Powder Bed Fusion using Large Language Models](https://mlsanity.com/paper/2505.13828/) (2025)
- [Polymer-Agent: Large Language Model Agent for Polymer Design](https://mlsanity.com/paper/2601.16376/) (2026)
- [EngiAI: Capability-Based Evaluation of Tool-Connected LLM Agents for Engineering Design](https://mlsanity.com/paper/2605.19743/) (2026)
- [Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy](https://mlsanity.com/paper/2506.00056/) (2025)
- [IM-Chat: A Multi-agent LLM Framework Integrating Tool-Calling and Diffusion Modeling for Knowledge Transfer in Injection Molding Industry](https://mlsanity.com/paper/2507.15268/) (2025)
- [QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with Vision Language Models](https://mlsanity.com/paper/2508.16661/) (2025)
- [From Papers to Property Tables: A Priority-Based LLM Workflow for Materials Data Extraction](https://mlsanity.com/paper/2604.07584/) (2026)
- [Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap Prediction](https://mlsanity.com/paper/2501.03456/) (2025)
- [Do Multimodal Large Language Models Understand Welding?](https://mlsanity.com/paper/2503.16537/) (2025)
- [In-Context-Learning-Assisted Quality Assessment Vision-Language Models for Metal Additive Manufacturing](https://mlsanity.com/paper/2511.05551/) (2025)
- [In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach](https://mlsanity.com/paper/2604.09889/) (2026)
- [LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing](https://mlsanity.com/paper/2605.03328/) (2026)
- [Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Mining](https://mlsanity.com/paper/2605.04278/) (2026)
- [Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuning](https://mlsanity.com/paper/2605.12516/) (2026)
- [Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling](https://mlsanity.com/paper/2606.11605/) (2026)
- [Kinematics-Grounded Agentic AI for Robotic Additive Manufacturing Process Planning](https://mlsanity.com/paper/2609.19347/) (2026)

## Abstract (arXiv)

Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-level outline but lack the capacity to return detailed instructions on manufacturing and material properties of novel alloys. Enhancing a smaller model with specialized domain knowledge may provide an advantage over large language models which cannot be retrained quickly enough to keep up with the rapid pace of research in metal additive manufacturing (AM). We introduce "AMGPT," a specialized LLM text generator designed for metal AM queries. The goal of AMGPT is to assist researchers and users in navigating the extensive corpus of literature in AM. Instead of training from scratch, we employ a pre-trained Llama2-7B model from Hugging Face in a Retrieval-Augmented Generation (RAG) setup, utilizing it to dynamically incorporate information from ~50 AM papers and textbooks in PDF format. Mathpix is used to convert these PDF documents into TeX format, facilitating their integration into the RAG pipeline managed by LlamaIndex. Expert evaluations of this project highlight that specific embeddings from the RAG setup accelerate response times and maintain coherence in the generated text.