Choose among steering, retrieval, weight updates—or hybrid RAG + fine-tuning—using knowledge vs adaptation axes.
Prompting vs.RAG vs. Finetuning? If you are building real-world LLM-based apps, it is unlikely you can start using the model right away without adjustments. To maintain high utility, you either need:
● Prompt engineering
● Fine-tuning
● RAG
● Or a hybrid approach (RAG + fine-tuning)
The following visual will help you decide which one is best for you:
Two important parameters guide this decision:
● The amount of external knowledge required for your task.
● The amount of adaptation you need. Adaptation, in this case, means changing the behavior of the model, its vocabulary, writing style, etc. For instance, an LLM might find it challenging to summarize the transcripts of company meetings because speakers might be using some internal vocabulary in their discussions. So here's the simple takeaway:
● Use RAGs to generate outputs based on a custom knowledge base if the vocabulary & writing style of the LLM remains the same.
● Use fine-tuning to change the structure (behaviour) of the model than knowledge.
● Prompt engineering is sufficient if you don't have a custom knowledge base and don't want to change the behavior.
● And finally, if your application demands a custom knowledge base and a change in the model's behavior, use a hybrid (RAG + Fine-tuning) approach. That's it!
