{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/164861"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/164861","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Evaluating Large Language Models as Circuit Design Assistants","abstract":"Large language models (LLMs) have exploded in capability in recent years. Previous attempts at AI systems for circuit design have had limited proficiency and been restricted in problem scope. LLMs, with their breadth of knowledge and reasoning ability, are a promising technology for a much more general-purpose circuit design assistant. We developed a dataset of electrical engineering problems and solutions with which to test an LLM-based system, since no such publicly available dataset exists to our knowledge; unmodified GPT-4 was able to solve 42% of the problems. We did a preliminary comparison of several knowledge bases to use for RAG knowledge injection, finding that a small, curated set of resources performed better than a larger, less-focused set of resources, though there were confounding factors which may have skewed the result. While this work is a start, significant future work is needed to continue developing an LLM-based circuit design assistant.","abstract_html":"Large language models (LLMs) have exploded in capability in recent years. Previous attempts at AI systems for circuit design have had limited proficiency and been restricted in problem scope. LLMs, with their breadth of knowledge and reasoning ability, are a promising technology for a much more general-purpose circuit design assistant. We developed a dataset of electrical engineering problems and solutions with which to test an LLM-based system, since no such publicly available dataset exists to our knowledge; unmodified GPT-4 was able to solve 42% of the problems. We did a preliminary comparison of several knowledge bases to use for RAG knowledge injection, finding that a small, curated set of resources performed better than a larger, less-focused set of resources, though there were confounding factors which may have skewed the result. While this work is a start, significant future work is needed to continue developing an LLM-based circuit design assistant.","abstract_has_math":false,"creators":["Cox, Matthew J."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Han, Ruonan"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-09","date_published":"2024-09","updated_at":"2026-07-22T22:22:19Z","subjects":[],"languages":[],"rights":["Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","Copyright retained by author(s)"],"rights_urls":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/164861","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Han, Ruonan"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Cox, Matthew J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-12T17:15:06Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-02-12T17:15:06Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Engineering in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/164861"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Large language models (LLMs) have exploded in capability in recent years. Previous attempts at AI systems for circuit design have had limited proficiency and been restricted in problem scope. LLMs, with their breadth of knowledge and reasoning ability, are a promising technology for a much more general-purpose circuit design assistant. We developed a dataset of electrical engineering problems and solutions with which to test an LLM-based system, since no such publicly available dataset exists to our knowledge; unmodified GPT-4 was able to solve 42% of the problems. We did a preliminary comparison of several knowledge bases to use for RAG knowledge injection, finding that a small, curated set of resources performed better than a larger, less-focused set of resources, though there were confounding factors which may have skewed the result. While this work is a start, significant future work is needed to continue developing an LLM-based circuit design assistant."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Evaluating Large Language Models as Circuit Design Assistants"]}]}],"canonical_facts":{"dc:contributor.advisor":["Han, Ruonan"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Cox, Matthew J."],"dc:date.accessioned":["2026-02-12T17:15:06Z"],"dc:date.available":["2026-02-12T17:15:06Z"],"dc:date.issued":["2024-09"],"dc:description.abstract":["Large language models (LLMs) have exploded in capability in recent years. Previous attempts at AI systems for circuit design have had limited proficiency and been restricted in problem scope. LLMs, with their breadth of knowledge and reasoning ability, are a promising technology for a much more general-purpose circuit design assistant. We developed a dataset of electrical engineering problems and solutions with which to test an LLM-based system, since no such publicly available dataset exists to our knowledge; unmodified GPT-4 was able to solve 42% of the problems. We did a preliminary comparison of several knowledge bases to use for RAG knowledge injection, finding that a small, curated set of resources performed better than a larger, less-focused set of resources, though there were confounding factors which may have skewed the result. While this work is a start, significant future work is needed to continue developing an LLM-based circuit design assistant."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/164861"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","Copyright retained by author(s)"],"dc:rights.uri":["https://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:title":["Evaluating Large Language Models as Circuit Design Assistants"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:22:19Z"}