In this blog, we dive deeper into the practical application of RAG (Retrieval-Augmented Generation) and fine-tuning by exploring real-world scenarios. If you’re deciding between these approaches for your AI solutions, this breakdown will clarify which to use based on specific system requirements.
We’ll analyze three examples:
- AI Financial Planning Assistant
- Needs dynamic, real-time data handling, conversational fluency, and financial knowledge.
- Verdict: RAG is the best choice, as it leverages real-time data and pre-trained conversational models effectively.
- Financial Information Extraction Bot
- Focuses on extracting and structuring financial domain knowledge, with no need for conversational ability or dynamic data.
- Verdict: Fine-tuning is ideal for tailoring the AI to this specialized task.
- AI Sales Bot
- Combines dynamic product data and specialized sales techniques.
- Verdict: A hybrid approach is optimal, using RAG for dynamic product knowledge and fine-tuning for sales strategies and tone.
For detailed information, please watch our YouTube video: RAG vs Fine-Tuning: A Practical Case Study
