This presentation was provided by William Mattingly of the Smithsonian Institution, for the eight and final session of NISO's 2023 Training Series on Text and Data Mining. Session eight, "Building Data Driven Applications" was held on Thursday, December 7, 2023.

1. Text and Data Mining
Building Data-Driven Apps

2. 1. Refresher on Vector Databases
2. Large Language Models
3. Retrieval-Augmented Generation (RAG)
4. Streamlit
5. Gradio
6. R Shiny
Goals

3. Multi-Modal
How does it work?

4. Large Language Models

5. Large Language
Models
What are they?
● Large language models (LLMs)
like GPT-4 are advanced AI
systems trained on extensive
datasets to understand and
generate human-like text.

6. Large Language
Models
What are they?
● Natural Language
Understanding (NLU)
● Natural Language Generation
(NLG)

7. Large Language
Models
What are they?
● Natural Language
Understanding (NLU)
● Natural Language Generation
(NLG)

8. Large Language
Models
Training
● LLMs are trained on vast
amounts of text data from the
internet, books, and other
sources. This training enables
them to learn language
patterns, context, and various
knowledge domains.

9. Large Language
Models
Where do they excel?
● Generating new data based on
their knowledge of existing
data.
○ Code
○ Essays
○ Images

10. Large Language
Models
Limitations
● Hallucinations - Generating
incorrect data
● Ethics and Biases
● Copyright Infringement

11. Retrieval-Augmented Generation

12. How tall is Wookie?

14. How tall is Wookie?

15. RAG
What is it?
● RAG allows for you to combine
the strengths of large language
models (LLMs) with vector
databases
● It limits the chances for an LLM
to hallucinate (generate fake
information)
● It uses a vector database to
find relevant material to a
query

16. RAG
What is it?
● RAG allows for you to combine
the strengths of large language
models (LLMs) with vector
databases
● It limits the chances for an LLM
to hallucinate (generate fake
information)
● It uses a vector database to
find relevant material to a
query
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