When it came to using Large Language Models (LLMs), it used to be all about prompt engineering, which was seen as the silver bullet to obtain the best possible answers with the help of generative AI. The limitations of this belief became apparent soon enough, and one began to turn to what has been termed context engineering. This involved inputting more advanced information from sources such as database summaries, retrieval pipelines, and contextual memory, among other things.
Given that LLM models have evolved considerably over time and today possess an enhanced ability to comprehend natural language, it is far less dependent on prompt engineering than in the past. The ability to apply strategic thinking in order to accurately describe the problems in question, ask pertinent questions, analyse AI-generated output and use it to achieve one’s objectives is what is required these days. The AI era does not advantage those who are past masters at using prompt tricks, but those who can bring critical thinking to the fore and combine it with domain expertise and an ability to make sound judgment calls. Creative people who can strategise with the help of a partner that thinks and is more than a chatbot will be the ones to call the shots in this new dispensation. So strategic thinking is back in business, and the days of mechanically hammering in formulaic prompts into unthinking chatbots are definitely over.
There was a time when prompt engineering had taken on a life of its own. It was all about skilfully using clever phrases and chain-of-thought tricks to somehow coax or trick generative AI models into offering up reasonably sensible output. Modern models happen to reason by default. It no longer has to be all about clever words; not so much the prompt but the thinking that goes into creating it that matters. Having a handle on the exact nature of the problem one is trying to solve and what it is that an optimal answer should look like helps. A query that is supported by a clear knowledge of one’s goals, the context in which it is required and the inherent constraints one might face will obtain the most useful information almost by default. In other words, strategic thinking is the way to go, not how good you are at using a predefined prompting structure. A victorious general is almost always marked by a superior strategic understanding of the battlefield, which helps them provide their troops with just the right brief for the latter’s best course of action.
An extensive prompt vocabulary is not what helps one leverage AI in the best possible way. It is instead the ability to clearly define problems, be able to catch answers that are even the slightest bit wrong and come up with a better iteration. It is not about how you talk to a machine, but how clearly you express your request to the model in question. Prompts are, of course, not entirely dead, but they have to be backed by clear thinking and not some kind of engineering. No place for snake-oil salespeople- it's back to strategic thinking.
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