PromptLayer

The best prompt engineers write highly dynamic prompt templates. PromptLayer’s templating system offers two powerful ways to build dynamic prompts: f-strings and Jinja2. ?? F-strings: Quick and simple, perfect for straightforward variable substitutions. Just wrap a variable with {brackets} ?? Jinja2: More powerful, enabling conditional logic, loops, JSON handling, and advanced formatting for complex prompts and structured data. These use double {{brackets}} Read our guide to prompt template variables: https://lnkd.in/eE6P-f5j

AI "Reasoning" Isn't Magic - It's engineering https://lnkd.in/eXqYqmfZ Smart AI behavior comes from structured thinking blocks, not just raw model capabilities. We now have "smart" and "dumb" models. Allowing the model to think, and think in a strategic layered way, creates slower but much much more advanced models.

Stop mixing prompts with everything else in your AI stack. Firas at Gorgias successfully uses a prompt CMS (powered by PromptLayer) to isolate the LLM layer—unlocking faster template migrations, cleaner experiments, and smoother scaling. His key insights: ?? Prompts deserve their own dedicated service ?? Decouple prompt management from RAG and orchestration layers Result: Teams move faster, changes get simpler, and your AI infrastructure becomes infinitely more manageable. Well said!

Biggest takeaways from my conversation with Jared Zoneraich, founder of PromptLayer, on Episode 3 of The Latticework: - The importance of prompting: one can have the most powerful calculator in the world — the question then becomes what do you type into it? How do you interact with the model to obtain the best possible output?? - Procrastination can be a good sorting mechanism for working on the things that are genuinely interesting? - Jared learned the importance of cross-pollinating ideas and cross-training in different contexts (and the power of sales) when he built a jeans company? - Just like Excel was a new paradigm of work that put a UI on top of a SQL database and rendered it such that you didn’t need to know SQL to use it, prompting is similar where it’s a new paradigm and skillset of work, one that has some overlap with ML engineering but not complete overlap (domain expertise plus overall communication skills and problem solving logic matters as well!) - Computational irreducibility — some problems will always require human input, certain kernels are just irreducible - 10x engineers are thorough to a different order of magnitude, often characterized by “how much of the team is reliant on you” - "There's a beauty of ignoring experts in a lot of things." Listen to the episode here: https://lnkd.in/epwM3wgD

Subject matter experts, not ML engineers, will drive the next AI revolution. https://lnkd.in/e2tmef-N Domain knowledge trumps technical skills: the best AI builders will be experts who learn prompting, not engineers learning domains. Excel-like democratization: AI tools will enable professionals to be 100x more productive by bringing domain expertise directly to AI development.

Reports of prompt engineering's death have been greatly exaggerated. https://lnkd.in/eZ82GyhH You can hire 100 software engineers, but what will you have them do? The blank page problem isn't going away. The future isn't about better models, it's about better knowledge transfer: domain expertise - not technical tricks - is the new frontier of AI development.

Prompts should be unit testable Agents should be unit testable AI should be testable. To do this, you need to version your prompts, agents, and workflows. Compare long context windows with chains of small prompts. Compare OpenAI with Grok with Anthropic

"Evals are your company's intellectual property" ~ Alexander Bricken Really enjoyed the Anthropic talk at the AI Engineer Summit. Great insights into evals, prompts, and making AI systems that actually work. My take-aways below ?? -> You will build a competitive AI product through good evals. But many people are making mistakes. Their datasets are too small. They don't represent users. They are flying blind. ??????? Set up telemetry to track performance. Design representative test cases. Include random but valid edge cases (like kids asking about Minecraft). Good evals help you navigate model capabilities systematically. -> Fine-tuning isn't a silver bullet. In fact, you should probably avoid it. Think of it as last resort, not first solution. ?? You are performing "brain surgery" on the model. ?? That's not easy. It's expensive and limits model reasoning. Only pursue after exhausting other approaches. Before starting it, define clear success criteria. -> Your toolbox is bigger than you think. Leverage it. Prompt engineering, context retrieval, caching, citations - all improve performance without fine-tuning. Start with basics before complex solutions.

Thanks to Anote for having us at the AI Day Summit https://lnkd.in/gQ73J3iN Jared gave a 15-min demo of our product. Check it out to see PromptLayer in action.

We are proud to power prompt engineering at Gorgias, the #1 conversational AI platform for e-commerce. Thanks for the kind words, Victor. Great working with you and the team! https://lnkd.in/eFupDP4J