Everyone's hands are in the game with LLMs, and the developments aren't slowing down. Especially when it comes to fine-tuning, RAG, or deployment, my head is spinning. Where should I start with these topics? For example, what are the things often overlooked when deploying? How do you all approach these? What books, resources, or practical experiences do you recommend? Let's explore together!
Things to know when working with LLMs
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The most critical step when starting to work with LLMs is laying a solid foundation. At first, I made mistakes with fine-tuning—for example, not just adjusting the model but also focusing on how you prepare the data and define metrics. If you use imbalanced datasets, the model reinforces biases like "he" and "she." So, start by taking a simple model (like DistilBERT) and fine-tuning it on your small dataset. Instead of manually tweaking hyperparameters like learning rate and batch size with Grid Search, use automated tools like Learning Rate Finder—it saves time and gives better results.
One thing often overlooked in deployment is scenarios that can silently degrade your model. Have you tested how your system reacts when requests to your API spike all at once? Once, when deploying a chatbot to production, I forgot that users would disconnect if response times exceeded 5 seconds. I later optimized it to keep responses under 2.5 seconds. My advice now: before going live, stress-test your system with load-testing tools like Locust or JMeter. A small change can lock up an entire service, so make sure to include monitoring (Prometheus, Grafana) and an automatic rollback mechanism. For resources, the book *Building LLM Applications with LangChain* and Hugging Face’s deployment guides are super helpful.