Get Cooking with ChatGPT

ChatGPT from OpenAI has been getting a lot of publicity recently. If you haven’t heard of it, ChatGPT is an acronym for Chat Generative Pre-trained Transformer. What does that mean? It’s a chatbot that’s been tuned through reinforcement and supervised learning. There has been much discussion about the accuracy and usability of ChatGPT.

ChatGPT Models

ChatGPT currently offers three different models for response generation. They have different capabilities and come with different pricing. The models are:

  • GPT-3, for understanding and generating natural language.
  • Codex, for understanding and generating software code.
  • Content filter, for determining whether text may be sensitive or unsafe

Being in the software industry, the Codex model is intriguing, but I have yet to see examples of auto-generated code that does exactly what I’m trying to accomplish. Therefore, having previous experience in the culinary industry I thought it would be interesting to try ChatGPT in that arena using the GPT-3 model. Let’s see what sort of recipes ChatGPT can generate and how true to the classic recipes they are.

There are four different model options available when using the overall GPT-3 model: text-ada-001, text-babbage-001, text-curie-001, and text-davinci-003. The models go along a speed/cost scale of fast & inexpensive with text-ada-001 to the most capable but slower & more expensive text-davinci-003. While fine-tuning these models is an option, it goes beyond the scope of this post. With that in mind, let’s look at what these different models can generate for us “out of the box.”

The Recipe

In classical French cuisine, there are five mother sauces on which other sauces are based. To test out ChatGPT’s recipe generation, let’s look at one of these classic sauces: Béchamel. Béchamel is a simple sauce, from a classic ingredient standpoint, in that it contains butter, all-purpose flour, milk, salt, and pepper.

Let’s write a brief Python application that we’ll use to access the OpenAI API and see what responses we get from ChatGPT.

First, we’ll need to get an account with OpenAI and an API Key. As a best practice to keep your API Key safe, using an API Vault, such as Pangea’s Vault is a great idea. At a bare minimum, assign it to an environment variable so you can access in your application but not need to save the key in your code file.

Second, we’ll need to install OpenAI using a package management tool such as pip or pipenv. I prefer pipenv, so running the following command will get that installed:

pipenv install openai

Now, in a Python file,, we’ll make a “How do I make bechamel sauce?” request to OpenAI using the four different GPT-3 models:

import openai
import os

openai.api_key = os.environ['OPENAPI_APIKEY']

# Choose the model engine
model_engines = ["text-ada-001", "text-babbage-001", "text-curie-001", "text-davinci-003"]

# The question passed into the API
prompt = "How do I make bechamel sauce?"

for model_engine in model_engines:

     # Response generation
     generator = openai.Completion.create(
          engine=model_engine,  # (REQUIRED) - the model to use
          prompt=prompt,  # (OPTIONAL) - the prompt that's passed to the API
          max_tokens=1024, # (OPTIONAL) - the max # of tokens to generate
          n=1,  # (OPTIONAL) - how many completions to generate
          stop=None,  # (OPTIONAL) - stop word string or array.
          temperature=0.5.  # (OPTIONAL) - the coherency of the generated response. Number between 0 and 2

     generated_response = generator.choices[0].text
     print("Response for " + model_engine + ": ")

In the response generator, we have a few options, and we’re defining the model engine, passing in the prompt, defining the number of tokens used from the API (default is 16), and how many completions are generated for each prompt (default is 1), any stop sequences we want to define to stop further token generation, and the temperature of the response.

What is the temperature? It defines the randomness (coherency) of the generated response. Lower numbers here make the responses more focused and higher numbers generate more random output. We’ll explore this in a bit. For now, here are the responses I got from the above script. Remember that we’re looking for an answer to the question of “How do I make béchamel sauce?”

text-ada-001 (.50 second response)

There is no one definitive way to make bechamel sauce. However, most likely you will need to use grated cheese, milk, and butter.

Hmmm, that doesn’t really seem helpful and while there are sauces based on a bechamel that include cheese the base sauce doesn’t.

text-babbage-001 (0.61 second response)

Place a pot of water on the stove to boil. Add a cup of sugar and cook until the sugar has dissolved. Add a cup of milk and cook until the milk has boiled off. Pour the sauce into a saucepan and stir in the bechamel sauce ingredients. Cook over low heat until thickened.

Again, not spectacularly useful, for classic béchamel with sugar being mentioned. Also, what is a “pot of water” exactly? 1 cup, 1 quart, 5 gallons? I’m not sure what “until the milk has boiled off” means in this context either. And then, a very cyclical instruction for how to make bechamel sauce: “stir in the bechamel sauce ingredients.”

text-curie-001 (.84 second response)

Bechamel sauce is a simple sauce made from milk and flour. Heat milk in a saucepan over medium heat until it starts to simmer. Whisk in flour until a smooth paste forms. Cook, stirring constantly, until the sauce thickens. Serve hot.

Getting closer here, but still not quite there. Using straight flour to thicken milk doesn’t create a great flavor. However, even without measurements, you could generate something here, right? Even with 100 gallons of milk being heated to a simmer you know that you must add in enough flour until a “smooth paste forms.” Still, far from a classical béchamel sauce.

text-davinci-003 (7.3 second response)

Bechamel sauce is a classic French sauce made from a roux of butter and flour, and a liquid, usually milk. Here is a basic recipe:


-3 tablespoons butter
-3 tablespoons all-purpose flour
-2 cups milk
-Salt and freshly ground pepper
-Pinch of ground nutmeg


1. Melt the butter in a saucepan over medium heat.
2. Add the flour and whisk until the mixture is smooth and bubbling.
3. Gradually whisk in the milk, whisking constantly to avoid lumps.
4. Continue to whisk until the sauce thickens, about 5 minutes.
5. Season with salt, pepper, and nutmeg.
6. Serve warm.

This is a recipe that one could work with. I’d have a few nit-picks with the instructions, but overall, it makes sense. Personally, I wouldn’t add the nutmeg and it isn’t explicitly classical, but I’ve seen it done.

Temperature Adjustment

As mentioned earlier, the temperature parameter adjusts the “coherency” of the generated output. You can set that parameter to a number between 0 and 2. Recall that the above script ran with a temperature parameter of 0.5. The output was, while not always helpful, at least coherent.

The output from the text-davinci-003 model was useful. Let’s see what happens if we alter the temperature parameter value.


Bechamel is one of the staples in employing tha classic “mother tastes” that understands fundamental techniques, which are included bases like Bechamel felt se as Milk, simple sugars /mutable faster blend together all seeds again serve holes along veggies traditional

That’s just the introduction, the rest of the “recipe” isn’t much better…

To make béchamel classic:

1 MELT 2 tablespoons worthwhile normal core puns international info and work users tub back stainless stre ‘tub fully restricted sized documents allowed uses principle-friendly views data

… and it goes on but becomes less coherent.


Let’s go in the other direction and reduce the temperature parameter value. Here are the results:

Bechamel sauce is a classic French white sauce made with butter, flour, and milk.


– 2 tablespoons butter
– 2 tablespoons all-purpose flour
– 2 cups milk
– Salt and pepper to taste


1. In a medium saucepan, melt the butter over medium heat.
2. Add the flour and whisk until the mixture is smooth and bubbly.
3. Gradually add the milk, whisking constantly until the mixture is smooth.
4. Bring the mixture to a boil, then reduce the heat and simmer for 5 minutes, stirring occasionally.
5. Season with salt and pepper to taste.
6. Serve over your favorite dish. Enjoy!

Another great béchamel recipe, and this time without nutmeg!

I don’t believe you. You’re an impasta!

As we’ve seen using OpenAI and ChatGPT can have some interesting results. You might wind up with some imposter recipes or you might wind up with some winners. That is true overall, in my opinion, for AI-generated content now though. Could you build an entire application strictly based on results from ChatGPT? Perhaps. Individual results may vary though.

As the models get better, get more use, and “learn” more, things might change. And recipe creation is just one small example of this technology. We see folks attempting to use it in areas such as courtrooms, health care, insurance, and more. In comparison, culinary uses seem safe. Just based on the varying results of a béchamel sauce recipe, I think we have a ways to go with this technology.


Assignment Operators in R – Which One to Use and Where

Whenever you start learning a new programming language, you must get accustomed to the language’s syntax. One of the first operators you’d expect to come across is the assignment operator for the language. Assignment operators are used to, well, assign values to variables. The R language has a few different ways to assign values. Let’s take a quick look and them and some of their differences and use cases.

Assignment Operators

R has five common assignment operators:

  • <-
  • ->
  • <<-
  • ->>
  • =

Many style guides and traditionalists prefer the left arrow operator, <-. Why use that when it’s an extra keystroke? <- always means assignment. The equal sign is overloaded a bit taking on the roles of an assignment operator, function argument binding, or depending on the context, case statement.

Equal or “arrow” as an Assignment Operator?

In R, both the equal and arrow symbols work to assign values. Therefore, the following statements have the same effect of assigning a value on the right to the variable on the left:

x = 42

x <- 42

There is also a right arrow, -> which assigns the value on the left, to a variable on the right:

42 -> x

All three assign the value of forty-two to the variable x.

So what’s the difference? Are these assignment operators interchangeable? Mostly, yes. The difference comes into play, however, when working with functions.

The equal sign can also work as an operator for function parameters.

x <- 42
y <- 18
function(value = x-y)

History of the <- Operator

Where did the arrow as an assignment operator originate? As you may know, the R language has its origins in the S language. S was originally influenced, in part, by APL. APL had its own keyboards which included arrow symbols.

APL Keyboard Layout - Assignment Operators next to the "P" key

The S language also didn’t have == for equality testing, so that was left to the single equal sign. Therefore, variable assignment needed to be accomplished with a different symbol, and the arrow was chosen.


There are some differences of opinion as to which assignment operator to use when it comes to = vs <-. Some believe that = is more clear. The <- operator maintains backward compatibility with S. Google’s R Style Guide recommends using the <- assignment operator, which seems to be a pretty decent reason as well. When all is said and done, though, it is like many things in programming, it depends on what your team does.

R is a great language for data analysis. If you’re interested in learning how to use R to explore data in a MongoDB database, please check out this blog post I wrote. There are many uses for it, and knowing a bit about the assignment operators and which one to use should help tremendously.Facebooktwitterredditlinkedinmail