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)

About Lamini

Lamini is the all-in-one open LLM stack, fully owned by you. At Lamini, we’re inventing ways for you to customize intelligence that you can own.

Speakers

Sharon Zhou, Co-Founder and CEO of Lamini

Dr. Sharon Zhou is the cofounder and CEO of Lamini. As a former Stanford faculty member, she led a research group and published award-winning papers in generative AI. Sharon teaches some of the most popular courses on Coursera, including Finetuning LLMs, in total reaching nearly a quarter million professionals. She received her PhD in AI from Stanford, advised by Dr. Andrew Ng. Before her PhD, she was an ML product manager at Google. She received her bachelor’s degree from Harvard in computer science and Classics. Finally, Sharon has served as an AI advisor in Washington D.C. and has been featured in MIT Technology Review’s 35 Under 35 list.

https://linkedin.com/in/zhousharon