Brainstorming

# Fine-Tuning, RAG, and Prompt Engineering: Comparing Approaches ## Key Methods 1. **Fine-Tuning**: Adapting a pre-trained model on domain-specific data 2. **RAG (Retrieval-Augmented Generation)**: Combining language models with external knowledge retrieval 3. **Prompt Engineering**: Crafting effective prompts to guide model outputs ## Comparison | Method | Strengths | Limitations | Best Use Cases | |--------|-----------|-------------|----------------| | Fine-Tuning | - Specialized performance<br>- Consistent outputs | - Requires significant data<br>- Resource intensive | - Domain-specific applications<br>- Long-term projects | | RAG | - Up-to-date information<br>- Factual accuracy | - Retrieval overhead<br>- Potential inconsistency | - Question answering<br>- Research assistance | | Prompt Engineering | - Quick implementation<br>- Flexible | - May be inconsistent<br>- Limited by model knowledge | - Rapid prototyping<br>- General-purpose tasks | ## Decision Framework 1. **Project Timeline**: Short-term → Prompt Engineering; Long-term → Fine-Tuning 2. **Data Availability**: Limited data → RAG or Prompt Engineering; Abundant data → Fine-Tuning 3. **Task Specificity**: General tasks → Prompt Engineering; Specialized tasks → Fine-Tuning or RAG 4. **Resource Constraints**: Limited resources → Prompt Engineering; Ample resources → Fine-Tuning or RAG