Re: rag - retrieval-augmented generation

Thanks Eric for all the links (I am exploring these and still these are
accessible), and the AI4LAM Zoom meeting.

Best regards



On Fri, May 10, 2024 at 11:05 PM Eric Lease Morgan <
00000107b9c961ae-dmarc-request_at_lists.clir.org> wrote:

> Parthasarathi Mukhopadhyay <psmukhopadhyay_at_gmail.com> wrote:
>
> > ...However, one interesting point to be mentioned here is the effect of
> prompt
> > engineering on a RAG pipeline. When I ask the same questions as Simon did
> > on the same set of documents in a similar kind of pipeline with prompt
> > engineering, the result shows some differences (see additional system
> > prompt in the snapshot):
> >
> > --
> > Parthasarathi Mukhopadhyay
>
>
> Yes, when it comes to generative-AI, prompt engineering is a real thing.
> Prompts are akin to commands given to a large-language model, and different
> large-language models have different prompts. The prompt I have been using
> in my proof-of-concept applications have this form:
>
>   Context information is below.
>   ---------------------
>   {context_str}
>   ---------------------
>   Given the context information and not prior knowledge, answer the query
>   Write the answer in the style of {speaker} and intended for {audience}.
>   Query: {query_str}
>   Answer:
>
> Where the placeholders (the things in curly braces) are replaced with
> values from the interface. For example, {context_str} is the content of
> documents pointing in the same vectored direction as the query. The
> {speaker} placeholder might be "a second grader", "a librarian", "a
> professor emeritus", etc. The same thing is true for {audience}. The value
> of {query_str} is whatever the user (I hate that word) entered in the
> interface.  The prompt is where one inserts things like the results of the
> previous interaction, what to do if there is very little context, etc.
> Prompt engineering is a catch-as-catch-can. Once a prompt is completed, it
> given as input to the large-languagae model for processing -- text
> generation.
>
> Over the past week, I have created five different generative-AI chatbot
> interfaces, each with their own different strengths and weaknesses:
>
>   * climate change - https://5c0af9ffadb4b3d2ba.gradio.live
>   * library cataloging - https://6a147d360a3fc1d7df.gradio.live
>   * Jane Austen - https://e7053a831a40f92a86.gradio.live
>   * children's literature - https://a10e1d2687be735f40.gradio.live
>   * What's Eric Reading - https://e462cd2ac6d1e35d1c.gradio.live
>
> These interfaces use a thing called Gradio (https://www.gradio.app/) for
> I/O, and they are supposed to last 72 hours, but all of them still seem to
> be active. Go figure.
>
> Finally, today I saw an announcment for a AI4LAM Zoom meeting on the topic
> of RAG where three different investigations will be presented:
>
>   * Kristi Mukk and Matteo Cargnelutti (Harvard Library Innovation Lab),
> Warc-GPT
>   * Daniel Hutchinson (Belmont Abbey College), the Nicolay project
>   * Antoine de Sacy and Adam Faci (HumaNum Lab), the Isidore project
>
> See the Google Doc for details: https://bit.ly/3QEzQ6f
>
> --
> Eric Morgan
> University of Notre Dame
>