Mitchell Hashimoto

Prompt Engineering vs. Blind Prompting

April 14, 2023

"Prompt Engineering" emerged from the growth of language models to describe the process of applying prompting to effectively extract information from language models, typically for use in real-world applications.

A lot of people who claim to be doing prompt engineering today are actually just blind prompting.1 "Blind Prompting" is a term I am using to describe the method of creating prompts with a crude trial-and-error approach paired with minimal or no testing and a very surface level knowedge of prompting. Blind prompting is not prompt engineering.

There is also a lot of skepticism about whether prompt engineering can truly be described as "engineering" or if it's just "witchcraft" spouted by hype-chasers. I think in most cases the skepticism is rooted in the fact that a lot of tweets and blog posts I've seen claiming to be on prompt engineering are really at best a thin layer above blind prompting.

In this blog post, I will make the argument that prompt engineering is a real skill that can be developed based on real experimental methodologies. I will use a realistic example to walk through the process of prompt engineering a solution to a problem that provides practical value to an application.

This whole post will be biased towards expecting text output, since text output is the primary use case I've had with language models. Certain techniques -- such as the testing techniques -- don't translate 1:1 to other types of outputs such as images. Everything in this post does work well for multi-modal inputs, however.

Table of Contents

What is Prompting?

Feel free to skip this section if you're already very familiar with the term "prompting" or understand what a "prompt" is.

For language models (imagine ChatGPT if this term is unfamiliar to you), the "prompt" is the user-generated input to the model. In ChatGPT, it can effectively be understood as the text box you type in. The language model then "infers" a "completion" to your prompt. For example, if I type "4 + 3 = " in ChatGPT, it will respond with "7" (probably). In this case, "4 + 3 = " is the prompt, and "7" is the completion.

"Prompting" is the act of using prompts as a way to extract desired information from a model. It is an attractive approach to extracting information because you don't need a large offline training set, you don't need offline access to a model, and it feels intuitive even for non-engineers. Prompting is just one method to tune a model.

Finally, "prompt engineering" describes a more rigorous discipline (as will be shown in this post) that aims to utilize prompting as a way to build reliable functionality for real-world applications. It differs from ChatGPT-style prompting because the prompts generated through prompt engineering are usually meant to be used repeatedly in high-volume, diverse situations in order to solve a specific problem reliably for an application.

The Problem

To start, you must have a problem you're trying to build a solution for. The problem can be used to assess whether prompting is the best solution or if alternative approaches exist that may be better suited as a solution. Engineering starts with not using a method for the method's sake, but driven by the belief that it is the right method.

For this example, we will pretend that we're a company that builds a calendar client. We'd like users to be able to enter events using natural language. For example:

Dinner with Alice next Tuesday at Taco Bell

CorpConf on 11/4

1:1 with Bob tomorrow at 10 AM

Language models might be a good solution to take this natural language input and extract structured output to describe the event that we can then use within our application.

There are obviously other potential solutions. We can utilize a set of regular expressions and string search to look for common phrases (on <Day of Week>, tomorrow, today, next week, etc.).

Language models have their own benefits: they might provide an approach that handles other languages better, or might handle typos or grammatical errors better, or may at worst be a good backstop if regular expressions fail. In any case, there is enough promise to continue pursuing prompting as a potential solution.

The Demonstration Set

Next, we have to put together a demonstration set. The demonstration set contains an expected input along with an expected output. This set will serve multiple goals:

  1. It will be used to measure the accuracy of our prompt. By using the input of a single demonstration, we can assert that we receive the expected output.

  2. It specifies what we expect the prompt inputs and outputs to look like, allowing us as engineers to determine if it is the right shape for our problem.

  3. We can use a subset of this demonstration set as exemplars for a few-shot approach if we choose to use a few-shot prompt. For those unfamiliar with the term "few-shot", few-shot is a style of prompt where examples are given in addition to the prompt. See here for a good overview of Few-Shot vs Zero-Shot prompting.

Point (2) above is extremely important. We need to have some general understanding of what input we expect and what output we expect, because on both sides of this is [usually] software that needs to ensure the data is in a certain format and will expect a certain format back out. This is no different from typical software engineering where we decompose a problem into a set functions that have some input/output expectations.

We can utilize our examples from before and expand them to be complete demonstrations:

Q: Dinner with Alice next Tuesday at Taco Bell
A: next Tuesday

Q: CorpConf on 11/4
A: 11/4

Q: 1:1 with Bob tomorrow at 10 AM
A: tomorrow

A note on demonstration set size: For this blog post, we only have three demonstrations. In reality, you probably want at least a dozen. The more demonstrations you have the better testing you can do, but also the more expensive it becomes due to token usage. At a certain size, it is often more economical to fine-tune a language model.

There are two important decisions made for the above demonstrations. For any prompting problem, you'll have to make similar decisions.

First, we are only extracting one piece of information. It may be tempting to try to get the model to extract our entire event such as event name, attendees, time, location, etc. and output it as some beautiful ready-to-use JSON or some other format. The model may be able to do this. But when approaching a new problem, I recommend decomposing it into a single problem first. This makes the problem more tractable, and will also eventually give you a baseline accuracy that you can use to benchmark whether the multi-output approach is actually worth it or not.

Second, our output is not transformed. We aren't trying to turn everything into a date or trying to ensure everything is capitalize correctly or so on. We're doing literal text extraction. There are fantastic deterministic libraries out there that can turn strings like "next Tuesday" into timestamps with extremely high accuracy. This isn't something we need a language model to do for us. So, let's just get out the rough date format ("next Tuesday", "11/4", "tomorrow") and solve the problem of turning this into a timestamp using traditional programming methods because it is a trivial problem. The simpler the output, the easier it will be to get higher accuracy.

Finally, a brief note on output decoding: LLMs will complete your prompt in various ways: it may be a complete sentence, it may add a period, it may be capitalized, etc. You should determine how perfect you want your output from the LLM to be and how much you're willing to normalize prior to validating your demonstration set.

For example, if I'm doing text extraction, I typically find it reasonable to trim whitespace and periods and lowercase the entire output. If I'm doing something more advanced like JSON generation, I might parse and re-encode the JSON in some deterministic order and style so comparisons are deterministic. And so on.

My recommendation: keep the output from the LLM as simple and flexible as possible, and perform some normalization operations in your application. Don't try to force the LLM to output exactly perfect formats to start. Performing too much "output shaping" in the LLM early on makes it difficult to separate an LLM's ability to perform some core task (information extraction in this case) from its ability to structure the output.

Prompt Candidates

We now come up with some prompt candidates. A prompt candidate is a prompt that we feel may elicit the desired behavior we want from the language model. We come up with multiple candidates because its unlikely we'll choose the best prompt right away.

For the sake of being an introductory level text, we will come up with prompts manually. To be effective, there is some basic knowledge prompt engineers should use when building prompts. For example, it is better to be assertive than to be defensive. It is often better to be clear and concise than it is to be repetitive and long. When building a few-shot prompt, equal distribution of labels matters, demonstrating the full set of labels matters, etc. When choosing exemplars, exemplars that the LLM was likely to get wrong typically perform best, exemplars have been shown to often perform best when ordered shortest to longest, etc.

Citations required! I'm sorry, I didn't cite the experimental research to support these recommendations. The honest truth is that I'm too lazy to look up the papers I read about them (often multiple per point). If you choose not to believe me, that's fine, the more important point is that experimental studies on prompting techniques and their efficacy exist. But, I promise I didn't make these up, though it may be possible some are outdated with modern models.

It is a dedicated post on its own to go over some of these techniques and it isn't the goal of this post. The goal of this post is to show the high-level end to end process and to show that there is an engineering method to extracting value from LLMs.

At this point, the goal is to come up with some good zero-shot prompts. The zero-shot prompts can be transformed into few-shot and those can be further transformed into chain of thought. And each of those can be further turned into batched prompts and so on. Therefore, since zero-shot is a base requirement to try, we focus on that.

Here are three prompt candidates I came up with:

Identify the date or day mentioned in the given text and provide it as the output.

Identify the date or day mentioned in the given event description.

Determine the date or day from each input and provide the output accordingly as a single word or date.

They're all reasonable prompts. Given any educated human, every prompt would likely yield very high accuracy. But language models are not human, so we can't automatically expect equivalent performance. I've shown before how very reasonable prompts can have abysmal performance. So our next step is to inform our decision by doing some testing and taking some measurements.

Prompt Testing

With a set of candidate prompts as well as a demonstration set, we can now measure accuracy. The best way I've found to do this today is to build a simple Python script using a library like LangChain. For my testing, I usually run through each demonstration and perform the following prompt template:

{{prompt}}. Q: {{input}} A:

I always test zero-shot first. I want to get a baseline accuracy metric. From there, you can then test few-shot and compare not only different candidates but different prompting types. And so on.

This has to be done per-model. The same prompt even on a more powerful model is not guaranteed to have the same or better accuracy; the accuracy can go down2.

The most basic result of prompt testing should be a table like below. You may also have additonal dimensions such as model (GPT-3.5 vs GPT-4 for example).

           | Zero-Shot | Few-Shot | ... |
Prompt 1   | 64        | 68       | ... |
Prompt ... | 44        | 52       | ... |
Prompt N   | 23        | 22       | ... |

This table shows prompt candidates on the Y-axis, and prompt types using those prompts on the X-axis. The value is the accuracy as a percentage of correct answers. Continuing the calendar app example, "prompt 1" as a zero-shot prompt with one demonstration may look like this:

Identify the date or day mentioned in the given text and provide it as the output. Q: CorpConf on 11/4 A:

And we expect "11/4" as the answer. The few-shot version may look like this:

Identify the date or day mentioned in the given text and provide it as the output.

Q: Dinner with Alice next Tuesday at Taco Bell. A: next Tuesday

Q: 1:1 with Bob tomorrow at 10 AM. A: tomorrow

Q: CorpConf on 11/4. A:

A note for experienced prompters: the few-shot example doesn't say something like "mimic the examples below." Experimental research has shown this doesn't reliably increase accuracy, so I like to test without it first to limit tokens. Second, the few-shot example exemplars don't ever show the "MM/DD" extraction as an example, which is poor form. In a real few-shot setting, demonstrating all styles of extraction can be important ( Zhao, et al 2021).

For certain types of problems, such as classification problems, you can use a confusion matrix (Strobelt, et al 2022) to visualize the probabilities of other labels and use that to determine if your label set can potentially be better tuned.

In addition to accuracy, you also want to measure tokens used, requests used, etc. All of this will have to be taken into consideration when choosing your final prompt.

Choosing a Prompt

Finally, you choose one of the prompt candidates to integrate into your application. This isn't necessarily the most accurate prompt. This is a cost vs. accuracy analysis based on model used, tokens required, and accuracy presented.

For example, you may find that the few-shot variant performs best, but its only 4% more accurate on the test set and requires 200% more tokens (effectively doubling the cost for current API-driven models). You may determine for your business that its worth being 4% less accurate for half the cost.

Or, you may decide to go back and test other approaches to increase accuracy. For example, you may try a self-consistency decoding strategy (Wang, et al 2022) on a lower cost model to see if that improves the accuracy enough. Sometimes, using more tokens on a lower cost model will save significant money vs. low-token usage on a higher cost model. For example, GPT-4 is ~15x more expensive than GPT-3.5 today. That means that you effectively have 15x more token budget to increase the GPT-3.5 prompt accuracy (caveats around rate limits noted).

For our example in this blog post, we may choose to go with the zero-shot version of Prompt 1 in our table because it has 64% accuracy with likely significantly fewer tokens. Maybe we decide 64% accuracy is good enough to at least populate an event template for our calendar application. For this particular problem, I think we can do significantly better than 64%, but that's just the number I used in this post.

Most Importantly, you have the data to be make an informed decision.

Trust But Verify and Continuous Improvement

Due to the probabilistic nature of generative AI, your prompt likely has some issues. Even if your accuracy on your test set is 100%, there are probably unknown inputs that produce incorrect outputs. Therefore, you should trust but verify and add verification failures to your demonstration set in order to develop new prompts and increase accuracy.

Verification is highly dependent on the problem. For our calendar application example, we may want to explicitly ask users: "is this event correct?" And if they say "no," then log the natural language input for human review. Or, we can maybe do better to automatically track any events our users manually change after our automatic information extraction3.

As a different example, if our prompt is generating code (such as a regular expression or programming language text), we can -- at a minimum -- try to parse it. Parsing should never be a security concern4, and it gives at least the most basic validation that at least syntax is correct. And again, if this verification fails, we can log the input and output, grow our demonstration set, and develop better prompts.

Verification also helps against adversarial prompting. Adversarial prompting is a whole topic unto itself, and I won't cover it in this post.

And Onwards...

This blog post demonstrates how developing a prompt can -- in my opinion -- be an engineering practice. It describes a systematic approach to identifying a problem, forming solutions, validating those solutions, and applying continuous improvement to refine those solutions.

Compare the approach in this post to "Blind Prompting" which relies on anecdotal experience and pervasive trial-and-error to arrive at some proposed solution, and often doesn't build the proper systematic infrastructure to reliably iterate on prompts as time goes on.

I want to note that blog post is very elementary. There are multiple places in this post that could be improved with more advanced techniques that are already well-known. Further, I didn't cover important topics such as adversarial prompting. As a specific example, there are more scientific approaches to choosing the best examples for a few-shot prompt, but I wanted to keep this post to the basics as much as possible. If you want to learn some more advanced techniques, Prompt Engineering by Lilian Weng provides a fantastic overview.

Additionally, everyone is rapidly moving to higher-order LLM integrations: prompt chaining, agents, etc. Some people argue that future innovations such as these and more will make human prompting obsolete. Regardless of if this is true or not, I am someone who believes learning things from "first principles"5 is valuable and I think that learning prompting techniques such as this has only improved my ability to utilize higher-order language model techniques. I also believe basic prompting such as this still enables better performance of higher-order concepts.


  1. A lot of "noisy" people, perhaps. I've met and learned from a LOT of fantastic prompt engineers who are really applying good engineering practices to the space. Unfortunately, a lot of the noise I see on Twitter and other platforms is often not that.

  2. Another citation required. Again, I'm being lazy, but this is based on experimental research I've read in multiple papers. You can choose to not believe me, just test it yourself.

  3. There are obviously privacy implications here. I am just sharing an example, the technique may not be appropriate depending on the real-world situation.

  4. YAML. 😐

  5. I realize actual "first principles" would be even lower level. I use the phrase "first principles" here in the common figure of speech to specify some arbitrarily low-level point on top of which more knowledge can be built.