PromptLayer

I’ve been using?PromptLayer?for the past year and it’s been a game-changer for the team. Excited to share a case study on how we use?PromptLayer?at?Speak! PromptLayer helps us prompt engineer to massively scale our operational capacity. Using PromptLayer, I was able to generate large volumes of curriculum content across multiple languages and skill levels via a series of complex chain prompting. Something that would have taken months took a single week! As we scaled the product into new languages this year, content and translation work also piled up. We used PromptLayer as a tool to build localized content & refine prompts. It saved us weeks per market launch. Check out the full case study here:?https://lnkd.in/gqRVP2g5

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

Building a startup is about dialectics. Extreme urgency / 10-year gameplan Obsession with details / imagination of the future Insatiable work ethic / celebrating wins PromptLayer team went out to celebrate a revenue milestone! Steak, some drinks, then back to working on the next goal ?? ?? Acknowledge wins, but don’t rest

Talking prompt engineering and demoing PromptLayer tonight with DataStax

ADVANCED PROMPT ENGINEERING: Fine-tuning Wrote up a how-to guide that goes through a few examples. Fine-tuning is best used for output format, tone adjustment, or complex reasoning 1?? Writing style fine-tuning We fine-tuned Mixtral 8x7B using personal notes and essays to mimic your personal writing style. Data-prep was the hardest part here. 2?? Workout plan generator We used gpt-4 to create training data and fine-tune a cheaper gpt-3.5-turbo model. Of course we used @promptlayer to do this in a few clicks. https://lnkd.in/eUwAStij

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

PromptLayer is looking for someone to help us teach the world about prompt engineering. If you love LLMs + building + want to share knowledge with the community reach out ? Responsibilities: - technical content writing - building llm projects Check out the PromptLayer blog to see some examples. Internship or part-time gig. Open for students and hackers ??

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