  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 $\sim$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.
