Hey everyone,

As we continue to witness the rapid evolution of Large Language Models (LLMs), I wanted to open up a discussion on the current landscape of LLM development what's working, what's changing, and what to expect next.

What are you using for LLM development?
Are you fine-tuning open-source models like LLaMA, Mistral, or Falcon? Building from scratch with frameworks like Hugging Face, LangChain, or OpenLLM? Or relying on cloud platforms like OpenAI, Anthropic, or AWS Bedrock?

Where do you see the biggest impact?
Some use cases I've seen gaining traction:

• Domain-specific assistants in legal, finance, and healthcare

• Autonomous LLM agents for task automation

• AI copilots for code, writing, and data analysis

• Enterprise search and internal knowledge bases

What are the biggest challenges you’ve faced?
Latency, cost, hallucinations, governance, data privacy? How are you addressing them?

Let's share tools, tips, architectures, and ideas. Whether you're a developer, product manager, or researcher—your insight is valuable.

Looking forward to your thoughts!

#LLMDevelopment #GenerativeAI #AIEngineering #MachineLearning #OpenSourceLLM #FineTuning #NLP #AICommunity #CustomAI #AIInfrastructure