Skip to Content

Generative AI with LLMs: Instruction Fine-Tuning vs In-Context Learning for LLMs

Learn the difference between instruction fine-tuning and in-context learning for large language models (LLMs), and how they enable zero-shot task generalization.


Fill in the blanks: __________ involves using many prompt-completion examples as the labeled training dataset to continue training the model by updating its weights. This is different from _________ where you provide prompt-completion examples during inference.

A. Pre-training, Instruction fine-tuning
B. In-context learning, Instruction fine-tuning
C. Instruction fine-tuning, In-context learning
D. Prompt engineering, Pre-training


C. Instruction fine-tuning, In-context learning


The correct answer is C. Instruction fine-tuning involves using many prompt-completion examples as the labeled training dataset to continue training the model by updating its weights. This is different from in-context learning where you provide prompt-completion examples during inference.

Instruction fine-tuning is a strategic extension of the traditional fine-tuning approach. Instead of training the model on conventional prompt-completion pairs, it is trained on examples of instructions and how the LLM should respond to those instructions. For example, the instruction could be “Write a summary of the following article” and the LLM should produce a summary as the completion. Instruction fine-tuning enables the LLM to generalize to new tasks that are specified by natural language instructions at inference time.

In-context learning is a technique that leverages the LLM’s ability to learn from the context of the input. By providing a few prompt-completion examples before the actual query, the LLM can infer the task and the desired output format from the examples. In-context learning does not require any additional training of the model, but it relies on the model’s pre-trained knowledge and reasoning skills. For example, the input could be a few examples of sentiment classification followed by a sentence to be classified, and the LLM should produce the correct label as the completion.

Generative AI Exam Question and Answer

The latest Generative AI with LLMs actual real practice exam question and answer (Q&A) dumps are available free, helpful to pass the Generative AI with LLMs certificate exam and earn Generative AI with LLMs certification.

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

    Ads Blocker Image Powered by Code Help Pro

    Your Support Matters...

    We run an independent site that is committed to delivering valuable content, but it comes with its challenges. Many of our readers use ad blockers, causing our advertising revenue to decline. Unlike some websites, we have not implemented paywalls to restrict access. Your support can make a significant difference. If you find this website useful and choose to support us, it would greatly secure our future. We appreciate your help. If you are currently using an ad blocker, please consider disabling it for our site. Thank you for your understanding and support.