Context Management
Today a two-for-one bargain: (1) an article on a very important topic about how to get the most out of programming LLMs and (2) an example of using ChatGPT to improve on something you have written.
On this second point, I wrote the article and then used ChatGPT to make improvements. I thought it would be an interesting example to show the original version next to the final output so I have appended the original at the end. I asked ChatGPT-4 to rewrite the article, improving spelling, grammar, and adding any points I might have missed. Here is the final result:
In the rapidly evolving landscape of Large Language Model (LLM) programming, mastering context management is not just an advantage, but a necessity. This article delves into the complexities of context management, shedding light on its importance, diverse applications, and the challenges faced in optimizing its utilization.
Understanding Context in LLMs
The context window in LLM programming is the segment of information that the model processes in a given request. It plays a critical role in guiding the LLM's response. Below are some key types of context:
Challenges in Context Management
Effective context management in LLM programming is fraught with challenges:
Technological Advancements and Outlook
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Software companies are now introducing tools for better context management and multi-request applications. However, much of this process is still manual. Leading adopters of generative AI, with robust development teams, are pioneering in this space, providing valuable insights into scalable context management strategies.
Ethical Considerations and Bias
It's crucial to address the ethical considerations and potential biases in context management. Ensuring diverse and unbiased input data is key to maintaining the integrity of LLM outputs.
As LLMs continue to evolve, the art of context management will become increasingly sophisticated. Understanding its nuances and challenges is vital for any organization looking to leverage LLM technology effectively. The future of LLM programming hinges not just on the technology itself, but on our ability to adeptly manage the context it operates within.
And here is my original draft:
Probably the most important problem to solve in structured LLM Programming (which I recently wrote about) is in managing the context window and all of the possible information components of context. The context window is the block of information that a large language model (LLM) will process in a given request. Context can be used in a number of different ways to instruct the LLM how to perform the request, here are a few examples:
You can likely imagine many more categories of context. Using these different types of context in constructing a series of prompts is the way that you develop your use of an LLM to achieve a consistent high quality result that achieves your desired outcome. But there are a number of challenges to address, here are a few examples:
We are beginning to see software companies introduce development tools to help manage context and develop multiple request applications but right now a lot of this work has to be done by hand. The companies that are the most advanced in adopting generative AI have development teams and application frameworks in place to support this work at scale.
GAI Insights Co-Founder, Executive Fellow @ Harvard Business School
1 年Love the article. When considering Marketspace interactions in general (e.g. those interaction that occur in a digitally enabled environment), context is vital for cognitive continuity of the "user". Context was a central point of our work on Marketspace (https://hbr.org/1994/11/managing-in-the-marketspace). I think it's fascinating that in working with our silicon conversation partner(s) in the LLMs, we carbon-based cognitors need to "remind" the LLMs or our context and intent -- both to drive quality of outcome and fewer harmful answers. As Stephen Wolfram has said, we need a science of LLMs and one branch of that science, I believe, will be how to stimulate a vast probabilistic model to improve answers and/or outcomes of an interaction with that model. Firms are so used to dealing with technology as a deterministic system (with some notable exceptions including trading operations at any asset manager) that our "policies" in the IT shop will have to encompass new types of risk/return.... #genai
Go-to-Market Leader | AI Automation Strategist | Author | Driving Growth Through Intelligent Solutions
1 年A sobering reminder of the importance of the various literacies.