AI

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Libraries

Basic Anatomy of a RAG-bot

Prompt

LLM

Fine-tuning (RLHF)

"You are a helpful librarian. Answer the following patron question about the Princeton Library. QUESTION: {{ question }}"

Basic Anatomy of a RAG-bot

Context

LLM

Fine-tuning (RLHF)

"You are a helpful librarian. Answer the following patron question about the Princeton Library using only information in the provided context. QUESTION: {{ question }} CONTEXT: {{context}}"

Prompt

"Firestone is open 8:30am - 6pm"

List all articles about the Soviet dissident movement written in the last five years but not authored by Ben Nathans

I don’t have real-time access to databases or the internet to provide the latest articles...

Basic Anatomy of a RAG-bot

Context

LLM

Fine-tuning (RLHF)

Prompt

knowledge base

Retrieval Augmented Generation (RAG)

• Texts are cut into chunks equal to the LLM's context size
• Text chunks are then transformed into numerical representations called text embeddings.
• Text embeddings that are similar to the question are retrieved and added as context to the prompt.

"Firestone is open 8:30am - 6pm"

"Small World opens at 11 am"

"The US Open is coming to Philadelphia"

"The library's main entrances is closed." 

When is the library open?

[0.332,0.774,0.563 ...]

List all articles about the Soviet dissident movement written in the last five years but not authored by Ben Nathans

Chain of Thought

"You are a helpful  library research assistant. Think step-by-step and help the user solve their research problem." 

To solve the problem.
* First, I would go to library.princeton.edu
* Select the articles tab 
* Then I would enter "Soviet dissident movement"
* adjust the Publication Date filter...

Agents & Tools

MCP

Search Agent

"I need to search 'Soviet dissidents' where type is article and date is greater than 2020"

results = { "title":"Soviet Dissidents and their Friends","author","Fred Tyming"}

Serp Tool

"Google Scholar search..."

 Chain

 Graph