🚀 Introducing: LLM Text Summarizer

I recently built a Python-based LLM text summarization tool that takes user-provided text and transforms it into a concise, structured summary. But the goal wasn't simply to connect Python to an LLM API. I wanted to understand what actually goes into building a clean LLM application. 🛠️ What I implemented: 🔹 Python-based modular architecture 🔹 Google Gemini & OpenAI API integration 🔹 Reusable summarize_text() function 🔹 Structured prompt engineering 🔹 Input validation & sanitization 🔹 API error handling 🔹 Environment-based API credentials 🔹 Configurable LLM providers and models 🔹 Automated testing with Pytest 🔹 Mocked LLM responses for unit testing 🔹 Git & GitHub version control 💡 One of my biggest takeaways: Calling an LLM API is the easy part. The real engineering starts around the API: → What happens when the input is invalid? → What happens when the API fails? → How do you protect API credentials? → How do you switch between LLM providers? → How do you test the application without making real API calls? → How do you keep the code reusable and maintainable? These are the details I'm learning to focus on while building AI applications. This project started from a simple client-style requirement and turned into a practical exercise in LLM application engineering. 🔗 Project: [https://github.com/muhdayaz157/llm-text-summarizer.git] I'm continuing to build and document practical AI/LLM projects as I improve my skills. What would you add to this project next — a web interface, PDF summarization, streaming responses, or something else? hashtag#ArtificialIntelligence hashtag#GenerativeAI hashtag#LLM hashtag#Python hashtag#AIEngineering hashtag#MachineLearning hashtag#NLP hashtag#OpenAI hashtag#Gemini hashtag#PromptEngineering hashtag#SoftwareDevelopment hashtag#GitHub

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