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If you don't know this,
you may be missing out on the next generation technology.
Now, most of the software technologies will move on the following discussion axis. Rag & Fine-tuning.
What are the differences between RAG and fine-tuning,
which are artificial intelligence personalization technologies?
Both are used to ensure that large language models specialize in a subject.
All artificial intelligence software services you see right now are obtained by RAG (retrieval augmented generation) or fine-tuning these large language models,
from the largest to the smallest, on certain subjects. (I explained their working logic in my previous article, you can go and look.)
For example,
while the current HR processes in your company are managed by a person or team, an LLM that you train with your own HR policies with
one of these methods can enable you to make all these processes faster, more efficient, more consistent and cheaper as an artificial intelligence service.
All your employees can find answers to all their questions about HR by consulting this artificial intelligence agent and can easily carry out
all processes with your HR software connected to this agent
So which is best for which situation?
Fine-tuning:
Trains an existing model with specialized data for a specific task and makes it highly accurate for repetitive, predictable queries.
Fine-tuning is best when the data is relatively stable and task-specific, such as customer support chatbots or sentiment analysis.
But it is still a more expensive method and is also quite difficult and time-consuming to update.
RAG:
Combines ingestion and generation using an external information source to dynamically respond.
This approach is useful when the data is extensive or frequently updated, allowing models to effectively answer complex or topical questions,
such as frequently asked questions or live news commentary. It is more affordable and easier to update than fine-tuning.
If you still haven’t figured out how to incorporate AI into your products, you are missing out on a big opportunity to compete.
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