Fine Tuning Vs RAG : Understanding when to use.
As I continue learning GenAi , One question that i found perticularly intersting is :
When should we fine-tune an LLM, and when should we use RAG?
Both techniques help us customize AI applications, but they solve different ploblems.
1. What is Fine-Tuning?
Fine-Tuning is the process of taking an already pre-treained LLM and traning in your own dataset to improve its performance on a specific task.
in simple words :
You can say fine-tuning teaches a model how to behave or perform a perticular task.
When should you use Fine-Tuning?
A. When you need consistent response patterns.
B. When you want specific writing style or tone.
C. When you need specialized task behaviour.
2. What is RAG(Retrieval-Augmented Generation)?
RAG is different from fine — tuning it is technique that allows an LLM to access external information before generating an output.
Instead of retraning the model whenever information changes, you update the knowledge base.
When should you use RAG?
A. When your application needs private company docs.
B. When you want answer grounded in external knowledge.
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C. When you want an LLM to work with internal databases or PDFs
Real-world example :
Imagine building an AI-powered resume analyzer.
You Want your application to:
- Analyze resume.
- Match resume with job description.
- Identify missing skills.
- Generate interview questions.
How would you use these techniques?
Using RAG:
Suppose a user upload a job description from Google.
Your application retrieves relevant job requirements and company-specific interview resources from its knowledge base.
It provides that information to the LLM to generate a more relevant analysis.
Using fine-tuning:
you train a model using high-quality resume evaluation examples so it learns your preferred evaluation pattern and produces more consistent assessments.
Both techniques solve different problems.
At the end if you want to teach a model-specific skill, use fine-tuning.
And if you want an LLM to access external knowledge, use RAG.
Thanks for reading at the end.
