I am going to create a language learning app using OpenAI APIs.
My Idea is that when I give the model a word, a phrase, or a sentence, the model has to tell me almost everything about the query, like the definition and synonyms, or translate it to my specified language, and after that, format it to the Anki flashcards app.
To respond accurately, I want the model to search for the words in dictionary websites I define, e.g., Longman Dictionary, and search phrases on the web, e.g., Google, and translate the sentences using its knowledge.
Some of my daily queries:
What does “{word or phrase or sentence}” mean?
Translate “{word or phrase or sentence}” into the “{language}”.
Explain to me the “{concept}”.
That was the whole idea, and now I have some questions:
1- How can I define the dictionary sources for the model to search into them when the query is a word?
2- How can I tell the model to search phrases on the Web?
3- How can I tell the model to use its own knowledge to translate text?
4- I have some general configurations for all model responses, e.g., my language level, to explain specifically for this level or harassment level; where should I tell the model for this?
5- Which model is the best choice for this use case?
5- If I put all of these customizations in prompts, it would be numerous tokens and cost a lot,
Is there any way to reduce costs with the above-expected results?
Hi there! Welcome to the developer community.
It’s great to see your enthusiasm for language learning and AI! However, I’d like to give you a bit of perspective on what you’re aiming to achieve. The field of natural language processing (NLP), combined with app development for language learning, is vast and highly competitive, especially when giants like Duolingo and Google have been working on similar problems for years with teams of experts and massive resources.
Here’s the reality: creating something that works well and feels “complete” will take years of dedication - not just to build but also to maintain, as the landscape of technology and APIs changes rapidly. On top of that, catching up with big companies in this space is extremely tough. They have access to more data, funding, and advanced research than any single developer can realistically compete with. That said, there’s still value in learning and experimenting with smaller, more focused goals.
If you’re still eager to explore this field, I’d recommend starting with smaller, manageable steps rather than trying to tackle a fully-fledged app from the get-go. For example:
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Focus on one feature, like building a dictionary integration or a translation module. This will help you test the waters and see if it’s something you enjoy.
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Explore OpenAI’s APIs and fine-tune smaller language models for specific tasks rather than creating an app that “does it all.” This will help you avoid overwhelming yourself with too many features at once.
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Understand that costs will be significant - not just in terms of money but also in time. Even if you manage to build a working prototype, keeping it updated with accurate data and ensuring it remains relevant will be an ongoing challenge.
If you’re still determined to move forward, I’d recommend focusing on building skills that will serve you well in multiple fields:
- Learn about NLP fundamentals and REST APIs to handle integrations like search engines (that alone might be a task that takes years - even with ChatGPT writing the code for that if you have no prior experience in scraping or using search engine APIs in general).
- Study AI architectures like Retrieve-Augment-Generate (RAG) to understand how to combine external data with generative AI.
- Explore smaller, open-source tools (like LangChain) or pre-trained models to experiment with specific language tasks.
That way, even if the project doesn’t pan out, you’ll have gained valuable experience and skills that you can apply to other opportunities.
To sum up: While this is an exciting space, it’s important to approach it with realistic expectations. Try not to aim for an app that competes with big companies right away - start small, experiment, and focus on learning instead of launching something huge right off the bat.
Feel free to ask more questions if you want help taking those first steps! 
Hi! I appreciate for your comprehensive response.
Your advice will never be forgotten.
The idea is good and even somehow unique because, unfortunately, we don’t have a specific service to cover this need and provide everything about a word or phrase, and people have to search in different old UI dictionaries and combine the results by themselves.
In the modern way, people usually do this using AI chat, which is still time-consuming.
Example:
Suppose that you’re reading a e-book and you find a word that is weird for you, you have to switch to your AI agent search for the word, wait for the result, and copy the result to your Anki card.
But the fact is that I don’t want to create this app for publishing or competing with companies.
It’s my personal and special need and idea to improve my language learning speed by removing time-consuming actions.
I’ve implemented this using Gemini, and it works well. It’s also open-source, and you can find it on my GitHub page, which is linked to my profile.
But it costs a lot of tokens because it doesn’t have special options.
However, I think that by using OpenAI APIs, I can achieve better results.
Because of lots of options and services that I can use.
I don’t think here I need NLP and I can achieve most of my requirements by RAG or function calling and web scraping.
My real confusion is how to achieve something like ChatGPT updating the memory to avoid passing general things, e.g., the format of answering questions every time to cost more tokens.
Also, is the 4o mini model enough for this task, or do I have to use 4o?
https://platform.openai.com/docs/guides/fine-tuning
Looks like you are looking for this then.
Happy coding