When developing AI agents, understanding prompt engineering is essential. Prompt engineering is the key to effectively working with LLMs. It involves crafting inputs that guide these sophisticated AI systems to generate accurate and relevant responses. Mstering skill ensures that developers can leverage the full potential of LLMs, making interactions with AI more productive and precise.

There is a. lot of hype surrounding prompt engineering, with numerous videos, blogs, and articles claiming to reveal its “secrets.” However, it’s important to be cautious of these claims. In reality, prompt engineering boils down to a handful core concepts, Understanding these foundational principles is more valuable than chasing after supposed hidden tricks.

Prompt engineering is a blend of art and science. The fact is, the same prompt can elicit different responses due to the complex probabilistic nature of LLMs. This inherent variability means that trial and error is a common part of the process. Developers must often tweak their prompts multiple times to get the desired outcome, which requires patience and a willingness to experiment.

Moreover, LLMs are frequently updated, which can introduce changes in their capabilities and outputs. These updates can sometimes improve certain aspects while worsening others, adding another layer of complexity to prompt engineering.

With all this in mind, let’s take a look at some of the key factors for successful prompt engineering.

Be Clear

When working with LLMs, clarity in your pompt is essential for generating accurate and relevant responses. An LLM needs to understand the nuances of your request to offer useful information. By being clear and detailed in your instructions, you se the stage for the model to produce responses that align closely with your expectations. There are several techniques you can employ to achieve this clarity and context in your prompts.

Details

To obtain a relevant response from an LLM, it’s important to provide important details and context in your requests. Failing to do so leaves the model to make assumptions about your intents. A more effective approach is to be specific and include key information in your prompt. For instance, rather than simply asking “Explain gravity”, a more effective prompt would be “Explain how gravity affects the orbit of the Earth around the Sun, using simple terms suitable for a high school student.” This level of detail guides the model to generate a response tailored to your specific needs.

Persona

A system message is a powerful tool when working with LLMs, as it allows the model to adopt a specific persona or role for its responses. This approach enhances the relevance and quality of the model’s output.

System prompt: “When I ask for advice, respond as if you are a seasoned business consultant with over 20 years of experience in the tech industry.”

Use Delimiters

Delimiters are valuable tools when working LLMs as they help to clearly demarcate different sections of text that require specific treatment. This technique can enhance the model’s focus and accuracy in processing information. Common delimiters include triple quotation marks or section titles.

For instance, when dealing with a document that needs summarization, you could use a prompt like “Summarize the text delimited by triple quotes.” You would then enclose the entire document within triple quotes. This approach effectively instructs the LLM to concentrate solely on the content within the specified delimiters, ensuring that it summarizes only the relevant text while disregarding any extraneous information.

Steps for a Task

When working with large language models, breaking down complex tasks into a sequence of explicit steps can improve the model’s ability to follow instructions accurately. This approach provides a clear road map for the model to process information and generate responses. Here’s an example:

Follow these steps to create a simple Python function:

Step 1: The user will provide a brief description of a function they need. Based on this description, create a function signature, including an appropriate name and parameters. Prefix this with “Function Signature:”.

Step2: Implement the function body with the necessary logic to accomplish the described task. Use clear variable names and add comments to explain any complex parts. Present the complete function, including the signature from Step1, with a prefix “Implementation:”.

Step 3: Provide a brief example of how to call the function with sample inputs, and show the expected output. Present this as a code snippet with the prefix “Usage example:”.

Another approach is to use recursive summarization. This is when you have documents that are too long for an LLM’s context window. That is, you can summarize different sections of the document. Then you will summarize the summaries.

Time to Think

When faced with a complex problem or calculation, taking the time to think it through step by step often leads to better results. This principle applies not only to humans but also to LLMs. Models tends to produce more accurate responses when they’re prompted to explain their reasoning before providing a final answer. This approach, often called a “chain of thought,” allows the model to work through the problem logically, much like a human would.

Incorporating step-by-step reasoning in prompts can enhance problem-solving and decision-making processes. By explicitly asking to “reason it out” or “think things though step by step,” we create a mental framework that provides for a more methodical approach. This approach not only improves the quality of responses but also makes the problem-solving process more transparent and easier to follow.

However, other types of prompts can be equally valuable in refining responses. For instance, asking “What can be done to improve this response?” encourages critical evaluation and identifies areas for enhancement. Similarly, prompting with “Are there things I should add?” can reveal overlooked aspects or additional considerations. These prompts, along with others like “What assumptions am I making?” or “What are potential counterarguments?”, foster a more comprehensive and nuanced approach to problem-solving. By using a variety of thoughtful prompts, we can guide both human thinking and AI responses toward more thorough, balanced, and insightful outcomes across a wide range of tasks and decisions.

Length of Output

When working with large language models, you have the ability to request outputs of specific lengths. You can specify the desired length in various units such as words, sentences, paragraphs, or bullet points. However, it’s important to note that while the model can generally adhere to these requests, its precision varies depending on the unit of measurement. Specifically, asking for a certain number of words may not yield highly accurate results. On the other hand, the model tends to be more reliable when asked to generate a specific number of paragraphs or bullet points.

Summarize teh text delimited by triple quotes as one paragraph.
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