RAG vs Fine-Tuning Decision Tool: Choose the Right AI Approach
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🧭 RAG vs Fine-Tuning vs Few-Shot
Interactive decision tool: answer 5 questions to find the optimal approach for your use case.
1 How frequently does your knowledge base change?
Real-time / daily
Data changes constantly (news, prices, inventory)
Weekly / monthly
Periodic updates, not real-time
Rarely / static
Knowledge base is mostly stable
2 How large is your domain-specific dataset?
Small (<1K examples)
Limited labeled data available
Medium (1K-50K)
Moderate amount of domain data
Large (50K+)
Substantial domain corpus available
3 What is your primary goal?
Maximum accuracy
Get the most precise answers possible
Minimize cost
Keep inference costs as low as possible
Full output control
Control tone, style, and behavior precisely
Lowest latency
Fastest possible response time
4 Do you need to cite sources / provide references?
Yes, always
Users need to verify information sources
Sometimes
Nice to have but not critical
No
Source attribution not needed
5 What is your team’s ML engineering capacity?
No ML team
We use APIs and off-the-shelf tools
Small team (1-3)
Can manage basic ML pipelines
Experienced team
Comfortable with training, evaluation, deployment
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rag:{title:’🏆 RAG (Retrieval-Augmented Generation)‘,color:’rag‘,
desc:’RAG is your best fit. It retrieves relevant documents at query time, so your knowledge stays current without retraining. Perfect for frequently changing data and source citation.‘,
pros:[‚Always up-to-date knowledge‘,’Source attribution built-in‘,’No training required‘,’Works with any LLM‘],
cons:[‚Higher latency (retrieval step)‘,’Requires vector database‘,’Retrieval quality is critical‘]},
ft:{title:’🏆 Fine-Tuning‘,color:’ft‘,
desc:’Fine-tuning is your best fit. With sufficient data and ML capacity, it gives you the most control over output quality, tone, and behavior. Best for stable knowledge domains.‘,
pros:[‚Maximum output control‘,’Lowest inference latency‘,’No retrieval overhead‘,’Best for specialized domains‘],
cons:[‚Requires training data & expertise‘,’Knowledge frozen at training time‘,’Risk of catastrophic forgetting‘,’Higher upfront cost‘]},
hybrid:{title:’🏆 Few-Shot Prompting (or Hybrid)‘,color:’hybrid‘,
desc:’Few-shot prompting (or a hybrid approach) is your best fit. With limited data or ML capacity, you get good results by providing examples in the prompt. Consider combining with RAG for best results.‘,
pros:[‚No training required‘,’Quick to iterate‘,’Low cost to start‘,’Easy to maintain‘],
cons:[‚Limited by context window‘,’Less precise than fine-tuning‘,’Token costs add up‘,’Example selection matters‘]}
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✅ Advantages for your use case
`;
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⚠️ Watch out for
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