What is In-Context Learning?
A method where models are guided to perform tasks using examples provided in the input prompt.
More about In-Context Learning:
In-Context Learning enables AI models to perform tasks by including examples or context directly in the input prompt. This approach leverages pre-trained models like PLMs to adapt to new tasks without additional fine-tuning.
In-context learning is particularly effective for tasks like question answering, prompt engineering, and retrieval fusion, where contextual examples guide model behavior.
Frequently Asked Questions
How does in-context learning differ from few-shot learning?
In-context learning provides examples in the input prompt, while few-shot learning involves fine-tuning with minimal labeled data.
What tasks are well-suited for in-context learning?
Tasks like semantic search, context-aware generation, and dialogue systems benefit greatly from in-context learning.
From the blog

IT Help Desk Automation with SiteSpeakAI
In a world that’s constantly evolving, having a robust IT help desk is no longer a choice but a necessity for businesses. But, how can you ensure that your help desk is able to respond to queries swiftly and accurately? The answer lies in automation, and one tool that is making waves in this domain is SiteSpeakAI.

Herman Schutte
Founder

Fine-tuning your custom ChatGPT chatbot
Finetuning your custom chatbot is a crucial step in ensuring that it can answer your visitors questions correctly and with the best possible information.

Herman Schutte
Founder