[Example] Unleashing the Power of Prompting

Large language models (LLMs) are incredibly powerful tools, capable of generating human-like text on almost any topic. But like any powerful technology, they can sometimes feel unpredictable or difficult to control. The key to unlocking their full potential? Mastering the art of prompting.

At its core, prompting is about providing the right input to get the desired output from your LLM. It’s a bit like starting a car - you can’t expect it to go anywhere without first inserting the key and turning the ignition. With LLMs, that “key” is your prompt.

Basic Prompting

The principle of “garbage in, garbage out” applies to LLMs just like any other system. A vague or poorly-worded prompt will likely result in an unhelpful or low-quality response. For example, asking an LLM to “write a poem” will get you…well, a poem. But asking it to “write a Shakespearean sonnet exploring the bittersweet feeling of nostalgia” gives it much clearer creative constraints to work within.

Contextual Prompting

Sometimes, you need to provide your LLM with some background information or context before asking it to perform a task. Think of it like talking to an expert consultant - you wouldn’t just blurt out a request without first explaining the situation. For a complex topic like “summarizing the history of the European Union” you might first give a short paragraph of context about the EU’s formation and core institutions.

Given the European Union's origins in post-World War II peace efforts and its evolution into a supranational organization with its own parliament and monetary system, summarize the history of the European Union

Interactive Prompting

Prompting doesn’t have to be a one-and-done process. In fact, some of the most effective prompting happens through an iterative, back-and-forth process. You ask a question, get a response, then refine or build upon that response with additional prompts. For example, you could ask an LLM to generate a high-level story outline, then interactively prompt it to expand on specific plot points, settings, or characters.

You are an interactive story generator,
Ask me enough questions, one at a time, to build out the plot, points, settings and characters.

Chain of Thought Prompting

For complex, multi-step tasks, you may need to explicitly guide the LLM’s step-by-step reasoning process. This is known as “chain of thought” prompting. Instead of asking for a direct answer, you break the problem down into smaller sub-prompts that lead the LLM through the logical steps required to arrive at the solution. This can help ensure it follows a sound reasoning process and doesn’t overlook key considerations.

Human: Generate a chain of thought for composing an engaging blog post titled something like: The Art of Cheese making
LLM: ... chain of thought ...
Human: Now generate the full blog post based on this Chain of thought
LLM: ... blog post ...

Other Prompting Techniques

There are many other creative prompting techniques that can be employed, depending on your needs:

• Few-Shot Prompting: Providing a few examples before the actual prompt, to steer the LLM’s output in the desired direction.
• Prompt Chaining: Combining multiple prompts together into longer “chains” for more complex tasks.

The possibilities are endless! Mastering the art of prompting is a bit like learning a new language - challenging at first, but incredibly rewarding once you gain fluency. It opens up a vast new world of capabilities with LLMs. So don’t be afraid to experiment and find the prompting approaches that work best for your use case. The power is yours to unleash!