PromptLayer’s Post

Speak is building the best way to learn a new language, powered by LLMs. As they expanded to over 10 new markets this last year, PromptLayer helped them generate content, refine prompts, and scale content workflows. Launching in a new language became quicker, and tasks that would have normally taken months took a single week. PromptLayer enabled non-technical teams at Speak, particularly in content and product, to iterate on AI features without requiring constant engineering support. Read the full case study: https://lnkd.in/enfJ98rZ

We are excited to sponsor the RAG++ hacknight next week in NY! There will be a special PromptLayer prize 🍰 See you there https://lnkd.in/eE25HZht

Starting a new tutorial serious of prompt engineering basics. Tip #1 is JINJA2. Extremely powerful. You might even remember it from the old web development days... Use it to swap out complicated Python code for simple jinja2 prompts → {% for item in user_info %} - {{ item.key }}: {{ item.value }} {% endfor %} The above will take in a JSON user_info and format it into a prompt. This let's you store more business logic in the CMS and less in code. Way easier to track. https://lnkd.in/eyDvRm6c

OpenAI just released fine-tuning for gpt-4o and gpt-4o-mini Try it out now on PromptLayer! Very powerful!

Everyone is talking about tool calling and structured outputs these days. It's the most important thing for a prompt engineer to know.. but every model does it a little differently. We put together a chart to compare the slight differences between models ✍️ Alternatively... you just store all your prompts in PromptLayer and they will be instantly runnable on any model. Even with tool calling 🔨 Read the full post https://lnkd.in/dv27M3-B