How to Make Your LLM More Accurate with RAG & Fine-Tuning
Should your LLM memorize everything or just look it up? Let’s break it down.
Imagine spending months preparing for an important exam. By the time test day arrives, you’ve internalized key concepts, and recalling them feels natural.
Now, contrast this with a different scenario: You’re faced with a brand-new topic, and rather than relying on memory, you quickly look up the relevant information in a book or online.
These two approaches mirror the two most effective ways to enhance Large Language Models (LLMs): Fine-Tuning and Retrieval Augmented Generation (RAG).
Fine-tuning embeds knowledge directly into the model’s parameters, like long-term learning.
RAG, on the other hand, acts like a dynamic reference system. It retrieves external data on demand.
But when should you use which? And how to these two methods work?
👉 Read the full article on Towards Data Science: How to Make Your LLM More Accurate with RAG & Fine-Tuning
What Is RAG? What Is Fine-Tuning?
The biggest limitation of LLMs is that they are static—once trained, they don’t automatically learn new things.
A model trained in 2024 won’t know about events from 2025 unless updated. Additionally, LLMs lack direct access to private or company-specific data unless specifically trained with it.
This is where RAG and fine-tuning come in.
RAG (Retrieval Augmented Generation) keeps the model unchanged but provides real-time access to external knowledge sources. This allows it to retrieve relevant, up-to-date information as needed.
Fine-Tuning permanently updates the model by training it with domain-specific data, embedding that knowledge into its core so it no longer needs external sources to recall information.
The Best of Both Worlds: Retrieval Augmented Fine-Tuning (RAFT)
For optimal performance, RAG and fine-tuning can be combined in a hybrid approach called Retrieval Augmented Fine-Tuning (RAFT).
How it works:
The model is fine-tuned with domain-specific knowledge, ensuring it understands specialized terminology and concepts.
RAG is then used to fetch real-time updates whenever needed, keeping the responses accurate and current.
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Have a great week, Sarah 💕