How to Build Real-World Applications Using LLMs in AI Development?

AI Discussions
1

I’ve been learning about large language models (LLMs) and how they are used in applications like chatbots, content generation tools, and AI assistants. However, I’m still unclear about how to move from theory to actually building real-world applications.

What I’m trying to understand is the complete process of using LLMs in practical scenarios. For example, how do you go from selecting a model (like GPT or other open-source LLMs) to integrating it into a working application? What tools, frameworks, or APIs are commonly used for this?

I’m also curious about how developers handle things like prompt design, context management, and improving response accuracy. How do you ensure that the application gives useful and reliable outputs in real-world use cases?

Additionally, how do businesses approach building scalable solutions using LLMs? Do they build everything in-house, or do they rely on AI Development Services to speed up development and deployment?

It would be really helpful if you could share real examples, project ideas, or best practices for building LLM-based applications that are actually used in production environments.

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Hey there!

Well I have been involved in a few projects where LLMs play a huge role in the development (and not just in te development, even in the deployment). However, I do apologise that I can’t specifically share the details of them but I can tell you it largely depends on the company and the expertise they have. But all of them agree on the same thing, cost optimisation, in other words how to get the best output with the smallest amount of tokens, calls, etc..

From what I’ve seen personally, it is the other way around, you select the problem you want to fix first then start looking for the best LLM that can be used, best in sense of speed, accuracy, cost, etc (it is usually a tradeoff so it falls down to the projects/company’s priorities). Tools can be libraries like LangGraph, LangChain and SDKs or even stuff from github directly like helicone AI. APIs is very case-specific, but most common for me was gemini and openrouter, they offer you free models that you can try out with fairly lenient quotas.

Regarding the second set of questions, from my personal experience, it was just rules-of-thumb, best practices, and being very specific to minimise hallucination. And if you can, setting up examples and actively trying them. In extreme cases, you would have to use few-shot learning (giving the llm previous solved examples). And sometimes, issues in the responses are fixed by pure coding and string manipulation.

That last question purely depends on the scale of the project and the size of the company. What I witnessed were startups building the prototype with services then governments or larger companies taking over and making the app in-house. I think in-house is becoming more popular generally though, people feel more secure when they feel like they “own” the LLMs.

Every company’s goals are different, so project ideas are plenty. Chatbots, text summarisation, document classification are all things that could be used in one way or another for almost any business. Most recently though, the agentic ai is becoming more popular so I would recommend taking a look at that.

I hope that answers your questions and let me know if you need anything else.

Best Regards