When we’re talking about grounding, we mean fact-checking the hallucinations of planet destroying robots and tech bros.
If you want a non-stupid opening line, when models accept they don’t know something, they ground results in an attempt to fact check themselves.
Happy now?
TL;DR
- LLMs don’t search or store sources or individual URLs; they generate answers from pre-supplied content.
- RAG anchors LLMs in specific knowledge backed by factual, authoritative, and current data. It reduces hallucinations.
- Retraining a foundation model or fine-tuning it is computationally expensive and resource-intensive. Grounding results is far cheaper.
- With RAG, enterprises can use internal, authoritative data sources and gain similar model performance increases without retraining. It solves the lack of up-to-date knowledge LLMs have (or rather don’t).
What Is RAG?
RAG (Retrieval Augmented Generation) is a form of grounding and a foundational step in answer engine accuracy. LLMs are trained on vast corpuses of data, and every dataset has limitations. Particularly when it comes to things like newsy queries or changing intent.
When a model is asked a question, it doesn’t have the appropriate confidence score to answer accurately; it reaches out to specific trusted sources to ground the response. Rather than relying solely on outputs from its training data.
By bringing in this relevant, external information, the retrieval system identifies relevant, similar pages/passages and includes the chunks as part of the answer.
This provides a really valuable look at why being in the training data is so important. You are more likely to be selected as a trusted source for RAG if you appear in the training data for relevant topics.
It’s one of the reasons why disambiguation and accuracy are more important than ever in today’s iteration of the internet.
Why Do We Need It?
Because LLMs are notoriously hallucinatory. They have been trained to provide you with an answer. Even if the answer is wrong.
Grounding results provides some relief from the flow of batshit information.
All models have a cutoff limit in their training data. They can be a year old or more. So anything that has happened in the last year would be unanswerable without the real-time grounding of facts and information.
Once a model has ingested a sizeable amount of training data, it is far cheaper to rely on a RAG pipeline to answer new information rather than re-training the model.
Dawn Anderson has a great presentation called “You Can’t Generate What You Can’t Retrieve.” Well worth a read, even if you can’t be in the room.
Do Grounding And RAG Differ?
Yes. RAG is a form of grounding.
Grounding is a broad brush term applied used to apply to any type of anchoring AI responses in trusted, factual data. RAG achieves grounding by retrieving relevant documents or passages from external sources.
In almost every case you or I will work with, that source is a live web search.
Think of it…
Source link
Disclaimer
We strive to uphold the highest ethical standards in all of our reporting and coverage. We blogs.grocliq.com want to be transparent with our readers about any potential conflicts of interest that may arise in our work. It’s possible that some of the investors we feature may have connections to other businesses, including competitors or companies we write about. However, we want to assure our readers that this will not have any impact on the integrity or impartiality of our reporting. We are committed to delivering accurate, unbiased news and information to our audience, and we will continue to uphold our ethics and principles in all of our work. Thank you for your trust and support.
Website Upgradation is going on for any glitch kindly connect at [email protected]
