Prompt-Engineering for Open-Source LLMs

Turns out prompt-engineering is different for open-source LLMs! Actually, your prompts need to be engineered when switching across any LLM — even when OpenAI changes versions behind the scenes, which is why people get confused why their prompts don’t work anymore. Transparency of the entire prompt is critical to effectively squeezing out performance from the model. Most frameworks struggle with this, as they try to abstract everything away or obscure the prompt to seem like they’re managing something behind the scenes. But prompt-engineering is not software engineering, so the workflow is entirely different to succeed. Finally, RAG, a form of prompt-engineering, is an easy way to boost performance using search technology. In fact, you only need 80 lines of code to implement the whole thing and get 80%+ of what you need from it (link to open-source repo). You’ll learn how to run RAG at scale, across millions of documents. What you’ll learn from this workshop: - Prompt engineering vs. software engineering - Open vs. closed LLMs: completely different prompts - Push accuracy by taking advantage of prompt transparency - Best practices for prompt-engineering open LLMs - Prompt-engineering with search (RAG) - How to implement RAG on millions of documents (demo) Take a moment to sign up for our short course: https://bit.ly/3HhK3jS Take a moment to sign up to our forum: https://bit.ly/3tTyyvV Workshop Slides: - http://tinyurl.com/Lamini-DLAI-Prompt-engineering Workshop Notebook: - https://github.com/lamini-ai/prompt-engineering-open-llms/ - https://github.com/lamini-ai/simple-rag About DeepLearning.AI DeepLearning.AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community. Take your generative AI skills to the next level with short courses help you learn new skills, tools, and concepts efficiently. About Lamini: