With the rapid rise of AI applications from chatbots to autonomous agents LLM development is quickly becoming a central focus for developers, researchers, and startups alike.

But building a useful LLM isn’t just about scaling parameters or pretraining on massive datasets. It’s about:

• Data curation: What strategies do you use for dataset quality and domain relevance?

• Model fine-tuning: Instruction tuning, RLHF, LoRA, QLoRA—what’s working best for you?

• Inference optimization: How are you deploying efficiently at scale? Are you using quantization, GPU offloading, or model distillation?

• Ethical alignment: How are you addressing bias, hallucinations, and safety?

• Tooling & frameworks: Are you working with Hugging Face, OpenLLM, vLLM, LangChain, or something else?

I’d love to hear about:

• Your LLM stack and what’s worked (or not)

• Challenges you’ve faced in development or deployment

• Tips for fine-tuning, evaluation, and feedback loops

• Open-source contributions or tools worth exploring

Let’s share knowledge, experiences, and maybe even code snippets. Whether you're experimenting with GPT-J, LLaMA, Mistral, or building on top of proprietary APIs, there’s a lot we can learn from each other.

What’s been your biggest insight (or headache) while working with LLMs?

#LLMDevelopment #MachineLearning #AI #OpenSource #GenerativeAI #NLP #AIEngineering #LLMs #TechStack #FineTuning