Fine-Tuning vs Prompt Engineering
Fine-tuning and prompt engineering are two primary methods for adapting language models to specific tasks or behaviors. They differ in complexity, flexibility, cost, and how deeply they influence the model.
Prompt Engineering
Prompt engineering involves crafting input prompts that guide the model to behave in a desired way—without changing the model itself.
Pros
- Fast and cost-effective
- Requires no access to model internals
- Easy to iterate and deploy
- Works well with large general-purpose models (e.g., GPT-4, Claude)
Cons
- Surface-level control
- Limited memory for complex or long-term tasks
- Susceptible to prompt injection or inconsistency
Fine-Tuning
Fine-tuning involves retraining a pre-trained model on domain-specific data, creating a custom version with embedded knowledge or behavior.
Pros
- Deep customization
- Improves performance on niche or repetitive tasks
- More consistent outputs for specialized domains
Cons
- Requires substantial compute and technical expertise
- Expensive (especially on large models)
- Risk of overfitting or forgetting base capabilities
- May require ongoing updates as data changes
When to Use Which
| Scenario | Recommended Approach |
|---|---|
| Rapid prototyping | Prompt Engineering |
| Domain-specific tone or style | Fine-Tuning (or few-shot) |
| Long-tail or repetitive tasks | Fine-Tuning |
| Public APIs or general chatbots | Prompt Engineering |
| Privacy-sensitive tuning | Fine-Tuning with local models |
Hybrid Strategy
Many production systems use both:
- Fine-tune a base model on domain-specific data
- Use prompt engineering to handle runtime variability and formatting
Related Notes
- Prompt Engineering
- Fine-Tuning vs Prompt Engineering
- Model Weights / Parameters
- Instruction Following
- Few-Shot Prompting
Model Weights and Parameters
Model weights and parameters are the core components that define a language model’s knowledge, behavior, and predictive capabilities. They are the
Prompt Engineering
LLMs Prompt Engineering Prompt Engineering LLMs Tokens Context Window Hallucination AI Agents Prompt Injection Model Weights and Parameters
Models by Meta
Meta (formerly Facebook) develops the LLaMA (Large Language Model Meta AI) family of open-source language models. These models are designed to be