Author: Shenggang Li
Originally published in the direction of artificial intelligence.
Practical comparison of context building techniques, templates and orchestrations in the modern LLM framework
Imagine you are in a cafe and ask for coffee. Simple, right? But if you do not specify details such as milk, sugar or a type of roast, you may not get exactly what you wanted. Similarly, when interacting with large language models (LLM), as you ask – hints – makes a big difference. That is why it is important to create custom (static) and dynamic hints. Adapted hints are as permanent recipes; They are consistent, reliable and simple. On the other hand, dynamic hints adapt based on the context, as is a qualified barista adapting the order of coffee based on mood or weather.
Let's assume that you are building a custom customer service powered by artificial intelligence. If you only use static hints, the bot can provide general answers by leaving frustrated users. For example, with the question “How can I help you today”? He is static and may be too vague. But dynamic prompt may contain the last user interactions, asking something like: “I see that you checked our order status. Would you like to help you follow in further”? This personalized approach can radically improve user satisfaction.
I will deal with practical comparisons of these fast methods, studying context building strategies, templates and orchestration tools. I will analyze the real world … Read the full blog for free on the medium.
Published via AI
