Professional Writing; Become a master of artificial intelligence • DigiKala Meg

Nowadays, by knotting artificial ielligence in all aspects of life, writing precise commands, or writing, has become one of the vital skills. A weak crop, even from the most advanced models of artificial ielligence, may receive ambiguous or inaccurate answers. If you wa to get the best results from ieracting with tools like GPT chat, adhering to the principles of writing is not a choice, but a necessity.

In this article, we examine the secrets of professional pragmatics, engineered techniques and top educational resources in a simple and practical language. From beginner to professional, everything you need to become a master of writing is here!

What is Pramat?

First, it is best to look at the most importa source of this field, Openai, to find out what the artificial ielligence is written and how to do the Pramet Engineering. Openai has defined the writing on its site as follows: Prompt is referred to as any command, question or input text that is given to a model of artificial ielligence to produce related and purposeful output. OpenAI emphasizes: “Prampet‌The model plays the role guide. The more accurate, clear, and structured guide‌Weter, it is more likely to receive the desired response. “

To get a better result in a professional writing you need to be careful:

• Determination of task: Determine exactly what you expect from the model (Example: Translation, Summary, Idea Production)
• Add the field (Coext): Add releva information to Praps to better understand the model.
• Reply formatting: Determine the output format example: List, paragraph
• Test and correct: Test Praps and improve it based on the results.

What is Pram Engineering?

If you have searched the word for writing, you will surely know the alphabet and may be looking for a puremark engineering, so it is better to get the basis of the story earlier. Here are the best tactics that can help you get better results than these models. It may be ieresting to know that you can use a combination of these methods to make it more effective.

• Divide the complex tasks io simpler steps

If it is very long or complex, turn it io differe parts. As in software engineering, the breakdown of complex systems io the smaller compones of a importa principle is the same in working with language models. Complicated tasks usually have more error and can be transformed io a set of simpler tasks. To do this, you can try the following ways to write:

Classification of the ultimate goal
Classify the ultimate goal to ideify the releva instructions. Simply put, assign releva instructions to each question by categorizing the purpose you follow from the Paramemat. In this case, you can use this method as a step. For example, for the question “Where does the elepha live?” You can go through the following steps:

1. Determining the main target: First, specify your main purpose of writing. For example, if you wa to know where the elephas live, your goal is to “habitat elephas”.
2. Ideification of subsidiaries: Then, ideify the main purpose related subdivisions. For example, “Elepha habitat” can include “tropical forests”.
3. Editing step questions: Design step -by -step questions based on subsidiaries. For example: In what habitat do elephas live? What are the characteristics of tropical forests? How do elephas live in tropical forests?
4. The use of releva instructions: Provide releva instructions for each question so that artificial ielligence can provide more accurate answers.

This method has two advaages:

Reduce error: Because only releva instructions are provided for each step, the probability of error is less.

Reduce costA: Because large -costs are more expensive, it is more cost -effective to use smaller periphes. This means that when we use smaller peripheits, the amou of calculations that the model should do is lower. Since most artificial ielligence services have processing costs based on the number of tokens consumed, the use of shorter peripheits is more financially cost -effective for you. In other words, if we use smaller bars, both the model works (because it has less computational load) and you pay less for API or service. This low -cost ieraction is optimized because it becomes more efficie and economically more economical. This method helps to optimize the ieraction with the model.

Summarize or filter the previous chat in long chat
Since the models have a fixed length, that is, in a chat more than a particular number, the message cannot be exchanged, and the conversation between the user and the ielligence cannot coinue to be infinitely. Summarize or filter a long conversation.

Summarize long segmeation documes
We cannot use models to summarize very long texts, like a book. Artificial ielligence models cannot summarize the whole text; Because they have a limited capacity and cannot process all information in one question. Therefore, the best way is to divide the text io smaller sections and summarize each section separately.

• Provide reference text

You may also have a question about artificial ielligence that you know, but it has answered you wrong! Or, if you are careful, almost rarely the GPT chat in response to users will use the answer “I don’t know”. So keep in mind that language models may confidely produce unrealistic answers, especially on specialized topics or when asking for resources and links. Just like a stude who performs better with his pamphlets, providing reference text to these models can help reduce errors.

To solve this problem you can have these requests:

Respond using reference text
If we can provide the model with the trust that relates to the curre question, we can poi out in our promise to use this information to adjust the answer. For example, if you are writing an article and wa to use a reference in your text, you can upload the reference PDF file to the chat and ask the model to answer your questions based on the file.

Answer by quote from reference text
Even after uploading a reference to artificial ielligence chat, it is still possible that the model does not directly reference from its response reference. You can ask the model to fix this problem, for example which page, which line or which part of the reference you provided has prepared it for you.

Most models today, such as Chat GPT -based chat, have added web chat to their chat chat, connecting the information they provide to the user with its link to each seence, and finally specifying the source. You can provide the source in the form of a file or writing to these models and ask them to specify which part of the source will answer or analyze you.

• Use of external tools

You can use other tools to offset the weaknesses of the model. For example, a text recovery system or code execution engine can help the model perform the calculations and call for APIs. Of course, this may be difficult for all ordinary users who are not familiar with the code.

If you iend to use this method, remember:

Using smart search
A model can use the external information resources given as part of the input. This will help the model provide more accurate and up -to -date answers. That is, releva and useful information from a data set or information sources (such as texts, articles, or even databases). For example, if a user asks a question about a particular movie, adding high quality information about that movie (such as actors, directors, etc.) to the model in ery can be useful. Using Embeddings, knowledge recovery can be implemeed efficiely; In this way, the related information is added to the model input at the time of the run.

Use the code to perform calculations

Language models alone are not reliable to perform long calculations with high accuracy. In cases where there is such a need, the model can be ordered and executed instead of performing its calculations.

Access to specific functions

The Openai AI can create a JSON command instead of directly responding to your function/code. Example:

Suppose we wa to save a note. This is the steps that happen:

The praise you write:

“New note called ‘Buy’ and the ‘Milk & Bread’ text

Instead of responding to the text, the model offers an output similar to what you see below:

You eer this json to your own code/program:

For example, your save_note (title, coe) function is executed and the note is stored.

You would send the result again:

For example, your program announces: “The note was stored ✅ »

Now the model can give a friendly answer:

«Your note was successfully saved! Need something else? “

In this case, the model does not have access to your database or system directly, but by producing JSON it helps you to do it yourself. For developers, it is easy to build this system (about 2 lines of code). Normal users do not need coding, they only use ready -made tools (such as telegram robots).

For example, if you have a meteorological app, the model can produce JSON uil the user asks “What is the air tomorrow in Tehran?”, Your system will connect directly to the API and give a precise answer! 🌤️

• Write transpare commands

Note that artificial ielligence is iellige but not a mind, so you should always make your request in detail in the most transpare way possible. If the outputs are too long, request brief answers. If they are very simple, ask for specialized writing. If you are not satisfied with the template, show the template you wa. The less the model has to guess your desires, the more likely it is to get the desired result.

The best tactics you can use to write a transpare prymema include:

Pay atteion to the details.
To get a fully releva response in the writing, you need to be sure that your requests include any importa details or coext. Otherwise, you will allow the model to guess what you mean.
Example:

Give to a particular character model
You can use a system message to determine the character that the model uses in its answers. For example, if you are looking for a question in English, you can ask the model to assume that an English language teacher was to answer your question in terms of English language knowledge. Try to use details for the character you create.

To write to the order of order.
Specify differe sections of Parabat. Separates such as trilogy quotes, XML tags, sections titles, etc. can help separate parts of the text that must be examined in a differe way.

Stage
Specify the steps required to complete a task. It is best to determine some tasks as a sequence of steps. Writing steps explicitly can make it easier for the model to follow them.

Provide an example
Guide how to respond to a few examples. Examples can always be a clear and explicit example for the model. For example, if you wa to add a letter or one shape between the words, it is best to do this manually for a few words and give it to the model for example.

Determine the desired length of the output
You can ask the model to produce outputs of a specified length. The desired output length can be determined in terms of the number of words, seences, paragraphs, bolt pois, and so on. Keep in mind, however, that the command to the model is not working to produce a certain number of words. The model can more confidely produce outputs with a specific number of paragraphs or bolt poii tips.

• To “think” to the opportunity model

You may have come to the poi that in some cases the time request model is responding to, and this is not a bad thing, corary to some people’s imagination. Just like when you are asked to multiply 2 – you may not know the answer immediately, but you can answer for a while. Models have more reasoning error when they have to answer immediately. The “iellectual chain” request before response can help the model more accurately argue.

If you wa to ask the model to think well about its answer, you can make these requests.

Ask for a solution
Sometimes we get better results when we say to the model clearly before it gets quickly, from the basics. Ask the model to tell you the steps of reasoning and how to get the answer. Of course, today’s Reason Fitcher in the GPT and Deepthink (R1) in the DipSic model allows you to see how the model is reasonable, and wherever you see the wrong argume you can ask the model to do it again.

Curve
Ask the model if it has missed something in the previous passage. Suppose we use a model to list excerpts from a source that are related to a particular question. After listing each excerpt, the model must determine whether it should coinue or stop. If the source is large, the model usually stops earlier and does not list all the releva excerpts. In this case, using follow -up questions can be achieved better performance of Prambtr to make sure that no excerpts are related to the pen.

• Systematic Testing of Changes

It is easier to improve performance when you can measure it. Sometimes a change in the Permapce may perform well on a few specific examples, but in a larger set of samples it can lead to weaker results. So it is necessary to define a comprehensive comprehensive test set to ensure the effect of changes.

You can follow the following:

Check the answer
Evaluate the model outputs by referring to existing standard answers. Evaluate the model outputs by referring to specific standards and changes. For example, if you are looking for Iran’s share in export exports, you should also check the official resources in addition to the model’s response to make sure the information is accurate.

Prambtt site

There are many sites to improve the professional career path that you can use to give an engineered porcelain to artificial ielligence models and get the most complete answer.

This tool is a periphery optimizer that converts your ideas io accurate and optimized commands for artificial ielligence models such as ChatGpt, Stable Diffusion, Midjourney, Claude and Gpt-4. Using advanced algorithms, this system provides you with the best results in the shortest possible time and can be easily usable.

Docsbot AI is a free tool that offers optimal and functional prizes for models such as ChatGpt, Claude and Gemini. This platform, with a variety of categories including writing and editing, business, training, technical, programming and creative, allows you to easily create the right commands for using artificial ielligence and have a great writing.

Webutulation is a tool that helps you produce accurate and comprehensive ChatGpt. This system allows you to adjust your questions in a structured way by providing differe options such as analysis, explanation, and comparison to get better answers and optimize writing.

AI Mind is a simple ierface that automatically generates optimized perpetrators by receiving an idea of ​​you. It also allows you to choose a response style (eertaining, professional or academic) to have commands to suit your needs and tastes.

Reliablesoft is a free criminal impact designed to produce high -quality text commands using models such as ChatGpt, Microsoft Copilot and Google Gemini. Using artificial ielligence technology, this tool helps you to easily and accurately create good commands for your projects.

GPT chat

The GPT chat is like the other models we’ve meioned so far, and taking the best string you should consider the steps that have been meioned so far. However, Openai offers practical tips for writing effective crops that can help get more accurate and high quality answers than artificial ielligence models. One of the main recommendations is that the commands are always transpare, accurate and detailed. The model is not a mind -reader and needs to provide all the information you need to understand your desires. If you have a complex question, it is best to divide it io easier steps so that the model can perform the task step by step.

Another OpenAI suggestion is to use the Chain of Though technique, which means that you ask the model to think about its own principles and stages of reasoning rather than quickly. This method is especially better for complex and analytical issues. Also, providing clear examples to the model for the format and style of answers are other importa techniques in pragmatism.

The Importance of Optimization Optimization is a repetitive way that is highly meioned; That is, it may be necessary to review and correct your crop after each response to reach the desired output. Finally, the use of structured requests and specified templates for answers such as Bolt Poi or table format can optimize your ieractions with the model.

Source: Openai