Chatgpt has taken the world by storm. No wonder that business owners through industries are eager to put it at work. They may have read some articles, found them on a simple example, then very excited about the idea of what they said: “Let’s do that! Let’s connect GPT in our system!”
It looks reasonable, right? Sales and marketing teams are likely to be the most enthusiastic, their hands together in charge of all the new expected customers who flow, but wait: What if you are the developer now is charged with publishing GPT?
Your boss tested that. They believe that everyone is doing it, so “It should be a piece of cake.” But what if I told you that to make Chatgpt works how you want and provide a real value for the final user, it is not such a walk in the garden? How can you transfer this message to your boss at work?
Fortunately, we wrote this article to help you do this, show how ChatGPT Online is far from preparing a GPT model for work. To make more accessible reading, make us the cookie and our main monster.
The Cookie Monster is that beloved blue creature of Sesame Street Sesame Street. Its love is very similar to the definition files of Chatgpt’s love for demands, while both try to stir joy – so we endured, let’s see whether we can conduct the measurement.
But be careful: If you think that using ChatGPT in your work will feel overwhelmingly like a child cartoon, it may quickly become a horror movie.
A top clip and discover the reason.
5 Cookie Monster (Important, ChatGPT!) Settlements: Decreased visions of your boss’s eyesight
1. GPT often loses the context of the conversation
First of all, we need to emphasize one decisive thing: Chatgpt learns from us during our conversation. However, it often seems to be talking to a very tangible grandfather who cannot remember what you said in the last sentence, so how do you avoid this?
You need to train the model to stop with memory holes. As you can see, GPT works to “Complete a wave”, meaning that if you want to teach them, you are presenting a mentor (i.e. the question), and the end model (i.e. the answer) generates.
But when the monster of cookies (i.e., GPT) flies a lot of cookies (i.e. your claims), it can forget quickly and forget the first cookie that ate it. For this reason, although you may have spoken well with Chatgpt, the cookie monster may not remember how it is intended to act.
2. Preparing training data takes time
How to prepare a training model depends on what you want to train on. Input data can be a set of texts, which are relatively simple to assembly.
However, remember that you cannot feed the model a wide text simultaneously (such as a full book or even an article). You should be treated in restrictions of the acceptable text of specific GPT versions, then cut your text accordingly.
Let’s return to Cookie Monster, which, at this point, can tell you how to taste your cookies.
When it comes to inference, it is more difficult than assembly because the model not only requires data but also logic. So, although GPT can receive any text, there is a great danger that it misunderstood contradictory or unclear instructions in the text.
Therefore, we must display the monster of cookies. How good cookies look like. Then he eats them and says, “Oh! This is how you want to bake cookies? Well, from now on, we will bake them like this.” Et Voila, Chatgpt will start baking cookies exactly as directed.
However, the taste may be a little. Usually this is due to two reasons: (1) an error in the input data or an insufficient number of “Question: Answer” or (2) including multiple tasks in one claim (such as classifying one text on different questions).
Let’s use an example to show you what we mean.
Suppose we want to train ChatGPT on the meal we must eat for breakfast. We want to choose Chatgpt for us based on our nutritional preferences. How many rules do you think we create? The answer is – the better (i.e., unless you want to eat eggs and bacon to eat breakfast every day!).
The good news is that you do not have to create all this data. You can always use ChatGPT to create a set of data for you. For example, you can give it a list of ingredients and then ask about the meals you can serve from.
You can then select people who really like you and ask any suitable for breakfast, lunch and dinner. After that, you can manually divide them into those that meet your criteria and then ask Chatgpt to refer to a set of questions whose answers will be specific meals.
Just remember Check precisely from the entire data collection To ensure that it creates data in line with your preferences and how you want to answer specific questions. And beware that the task becomes increasingly complicated if you want to be an expert GPT (for example, law or medicine).
In such a case, you will need a person with experience in the field to check your data set.
3. The provider may raise the price
Your boss needs to remember that the price of third party tools is rarely fixed. Openai can always raise its prices, so even if it costs $ 20 a month to reach today, this may change soon. At the same time, long -term costs depend on how to use GPT.
You may have to pay against a set of distinctive symbols to use in specific claims or completion, and prices may vary depending on the form you choose.
verify Current Openai pricing For the idea of prices.
4. The service may be temporary * decreases in non -communication mode
You also don’t have any control of the time to stop working, so this treated your service plan. Stopping work is an unavoidable risk, but you can create an emergency plan so that when users face it, they see something more than just a mistake.
5. Chatgpt is known to make things
Whatever you do: do no Trust everything he says ChatGPT.
Although the model can use previous knowledge to answer questions that are not related to your training data (such as “Who is the President of the United States of America?), If you ask,” Give me a recipe for cookie definition files with soap “, you may be surprised to respond. Want to see what we mean?
Check the response below:
In fact, this type of response is unexpected, indicated by MAćkowiak, a machine learning engineer at dlas.ai: “It is difficult to verify the accuracy of the LLMS models (LLM) is a larger task.”
“The same model is very general and can be used for many estuaric tasks. These models are trained on huge amounts of human data and interactions in the ring, and thus to properly verify the quality of this model, we will need to obtain a number of evaluation samples on the same arrangement of the size and in a similar process. This is likely to be not achieved for small institutions. “
GPT complicate from the cookie: a review menu to measure your work burden
So you want to use GPT, but you are not sure how much the work is involved. Well, you are lucky. We have prepared a multi -options for your manager to take them to better understand the offered task. Each response shows the amount of work required, and is measured in “cookies for developers”.
“The complexity of GPT” for the cookie definition file
Reference menu to measure your work burden
Click here to download
The higher the number of cookies you get, the more time, the effort, the more you will need to accomplish the task. Just make sure that your boss knows exactly what they need and the goals of the work they want to achieve.
This will make sure to focus on the right aspects.
Storm bread: a recipe for the success of GPT
Correct, what after? Well, all this depends on the complexity of your project (also known as: the number of cookies resulting from the test).
Check your number, then think about the following:
5-10 Cookies: The slightest complexity
If the simple solution is all you need, lucky! Just link yourself to GPT-4 and learn as you go! But be careful: this solution is only suitable for internal test. If you want a solution to the customer, we suggest you think twice!
10-15 cookies: medium complexity
Well, the project is not easy as it seemed for the first time, but there is a good opportunity to deal with it. Before you start the work, learn about the application programming interface, read the documents, and do some control and test. And when you find a mistake, fix it, then test again.
15-25 cookies: very complicated
Wah, this will be a challenge! Start by identifying the application programming interface, read the documents, then analyze and set up your data (remember the “Complete Permit” base). Make sure to consider the above risks (such as price changes and breakdown), and perhaps, Just perhapsThink about building your LLM.
Whatever you do: Do not forget to test all the edge cases, and do not trust the model blindly after a good prediction. Oh, and if you encounter any problems, you just have to consult a machine learning specialist, we are always happy to help.
good luck!
The main meals to implement GPT
We hope to help you see how GPT’s posting is not a child’s play. However, this does not mean that it is ashamed of it.
Regardless of your data collection and your team’s experience, “Start Small” and reduce the risk of something wrong. Also, forget UX/UI until you know how well the form is well set (or if it is a sword with your expectations).
Instead, focus on setting your goals before setting the form until you know the main performance indicators that you are working on-and as always the case: don’t hesitate to call Dlas.AI if you want to support when using this strong tool.
We are already working with many customers on similar projects, and we will be happy to start working with your company as well.
