Doc Assistant with Search in Adobe Data Distiller Guide

Absolutely! I just turned it on, and it's quite good that I'm already using it myself. It’s been incredibly helpful—any questions it can't answer help identify areas where I'll add more content. It's precise and provides just the right follow-up questions. You’ve got to give it a try!

In case you're wondering where this is all headed, the way we search for information is about to be disrupted. The race is on to develop alternatives to traditional web search algorithms.

LLM-based document assistants will play a key role in this shift by enhancing document retrieval, summarization, and question-answering through integration with search functionalities over the long term.

1. LLM-Enhanced Search: LLMs can understand queries contextually, allowing for more relevant document retrieval. They can perform semantic searches using vector-based search engines, finding documents based on meaning rather than keywords.
2. Retrieval-Augmented Generation (RAG): In RAG, LLMs work with search engines to retrieve relevant documents and then generate responses by summarizing or synthesizing information from those documents.
3. Search System Integration: LLMs can use vector databases for finding semantically similar content, hybrid search methods for better accuracy, and metadata filtering to refine search results.

Hopefully, this will spark inspiration for those experimenting with integrating LLMs into enterprise search.

Head over to https://data-distiller.all-stuff-data.com/

We have to let it fail, and that's okay! Its scope is limited to the documentation and a bit of creative interpretation. However, it's fact-based and won’t leave you stuck. Instead, it offers new questions and related content to explore.

You should never rely entirely on a doc assistant without doing your own exploration and testing. However, if you're in the process of learning, simply click on the search icon and dive deeper into the topics that interest you.