Solutions Architect @ ALPHA10X | Bridging Software, ML and Data Science to Empower Companies with AI Solutions
How do you get the best results from LLMs? After months of hands-on work, I've found that simpler approaches often outperform complex ones. Let's break down the main strategies, from basic to advanced: Basic Prompting: Just tell the LLM what you want—clear, straightforward instructions. Quick to set up, and often all you really need. I've seen this work great for a lot of tasks. Few-Shot Learning: Show the model 2-3 examples of what good output looks like. Like training a new team member by demonstrating the task first. This small extra step can make a big difference in output quality. RAG (Retrieval Augmented Generation): Give your LLM access to your specific data or documents. Great for when you need accurate, up-to-date responses based on your company's information. Fine-Tuning: Teaching the model your specific domain knowledge and requirements. It's more work, but sometimes worth it when you need highly specialized behavior from the LLM. Pretraining: Building your own LLM from scratch - Unless you've got Google-sized compute power and a few millions to spare, you might want to stick to the options above! 😄 What I've Learned: - Start with basic prompting - it's more powerful than most teams expect - Add few-shot examples when you need better accuracy - Save fine-tuning for when you really need it - Don't overlook RAG - feeding relevant context to your model can dramatically improve results You can solve most use cases with good prompting and RAG. No complex fine-tuning needed. Remember, less is more. #LLM #Development #AI
Maryam Guessous
2mo
Very Interesting Soufiane! 👏👏
Data & Analytics | Nestlé Nespresso | EMINES-UM6P
2moTry assistant api and start by defining persona in prompts then linking vectorised data to your assistant available with gpt4o version , very easy to setup and use , gives an incredible output, but comes with a cost !