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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Semantic Search of FDA Guidance Documents Using Generative AI.

Scott Proestel¹, Linda J B Jeng², Christopher Smith¹

¹Division of Biomedical Informatics, Research, and Biomarker Development, Office of Drug Evaluation Sciences, Office of New Drugs, Center for Drug Evaluation and Research, FDA, 10903 New Hampshire Ave., Silver Spring, MD, 20993, USA.

Therapeutic Innovation & Regulatory Science

|June 14, 2025

Summary

Generative artificial intelligence (AI) can help find information in FDA guidance documents, but accuracy is still a concern. While AI can cite sources, further research is needed before it can be fully trusted for drug development decisions.

Keywords:

Document search FDA guidance Generative AI Large language model

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Area of Science:

Regulatory Science
Artificial Intelligence in Medicine

Background:

Generative artificial intelligence (AI) offers transformative potential for accessing information crucial to regulating human drug and biologic products.
Efficient information retrieval is vital for regulatory processes.

Purpose of the Study:

To evaluate a generative AI application with retrieval-augmented generation (RAG) architecture for accurately answering questions based on FDA guidance documents.
To determine the efficacy of different large language models (LLMs) within a RAG framework for regulatory information retrieval.

Main Methods:

Five LLMs (Flan-UL2, GPT-3.5 Turbo, GPT-4 Turbo, Granite, Llama 2) were tested with the Golden Retriever RAG application.
Models were configured for precise answers (low temperature) to ensure reliable regulatory guidance.

Main Results:

GPT-4 Turbo demonstrated the highest performance among the evaluated LLMs.
GPT-4 Turbo provided correct responses with additional helpful information 33.9% of the time and fully correct responses 35.7% of the time.
The RAG application successfully cited the correct source document in 89.2% of cases.

Conclusions:

Generative AI applications can expedite the retrieval of information from FDA guidance documents.
The risk of incorrect information necessitates further refinement of AI models before widespread adoption in critical drug development decisions.
Prompt engineering and parameter tuning may enhance the accuracy and completeness of AI-generated responses.