In the midst of the generative The explosion of AI has innovation directors strengthening their companies’ IT departments in their quest for custom chatbots, or LLMs. They want ChatGPT, but with domain-specific intelligence that supports massive functionality, data security and compliance, and improved accuracy and relevance.
The question often arises: should they build an LLM from scratch, or refine an existing LLM with their own data? For most businesses, both options are impractical. This is why.
TL;DR: Given the right order of directions, LLMs are remarkably smart at bending things to your will. The LLM itself or its training data do not need to be modified to adapt it to specific data or domain information.
Exhaustive efforts in constructing a comprehensive ‘prompt architecture’ are advised before considering more expensive alternatives. This approach is designed to maximize the value extracted from a variety of prompts, improving API-powered tools.
TL;DR: Given the right order of directions, LLMs are remarkably smart at bending things to your will.
If this proves insufficient (in a minority of cases), Than a process of refinement (which is often more expensive due to data preparation) can be considered. Building one from scratch is almost always out of the question.
The desired outcome is to find a way to use your existing documents to create customized solutions that automate the execution of common tasks or answering common questions accurately, quickly, and securely. Prompt architecture appears to be the most efficient and cost-effective way to achieve this.
What is the difference between rapid design and tuning?
If you’re promptly considering architecture, you’ve probably already explored the concept of fine-tuning. Here is the key distinction between the two:
While fine-tuning involves adjusting the underlying fundamental LLM, fast architecture does not.
Refining is a substantial undertaking that involves retraining a segment of an LLM with a large new data set – ideally your own data set. This process infuses the LLM with domain-specific knowledge, in an effort to tailor it to your industry and business context.
Prompt architecting, on the other hand, is about leveraging existing LLMs without changing the model itself or its training data. Instead, it combines a complex and cleverly designed series of prompts to deliver consistent output.