LearnAI

Navigation

Introduction to AI

Machine Learning

Deep Learning

NLP & LLMs

Computer Vision

Generative AI

Reinforcement Learning

AI Ethics & Governance

Tools & Frameworks

General

Fine-tuning vs In-context Learning (Advanced)

Choosing between fine-tuning and in-context learning depends on cost, performance, and maintenance trade-offs.

Trade-offs

  • Fine-tuning: Better for consistent, repeated tasks; higher upfront cost; needs dataset curation and retraining.
  • In-context learning: Fast iteration with few examples; limited by prompt size and higher per-query token cost.

Practical guidance

  • Use fine-tuning for owned models where latency and per-query cost matter.
  • Use in-context learning for rapid prototyping, personalized prompts, or when dataset curation is expensive.

Hybrid approaches

  • Use small fine-tuned models for base behavior and augment with RAG or in-context examples for edge cases.