PromptLayer
@promptlayer
Your AI engineering workbench. Prompt versioning, evals, agents, and observability 🍰
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Announcing our seed fundraise! And that we are hiring in NYC!
Don't learn to code, learn to prompt engineer. Excited to announce our $4.8M raise led by @neversupervised at ScOp, @badboyboyce at Stellation Capital, along with @jbrowder1, @gokulr, @benln, @romainhuet, @OfficialLoganK, @ByrneHobart, @akilian,@GabeStengel and operators from…
Webinar mode We built a lot of internal AI features. Talking through some of the lessons
In the trenches: What we learned building AI tools for ourselves and our customers x.com/i/broadcasts/1…
In the trenches: What we learned building AI tools for ourselves and our customers x.com/i/broadcasts/1…
LLMs behave better when you speak in their native idioms. JSON for structure. Markdown for clarity. XML for hierarchy. Chat format for memory. Shape matters more than style. Read our post on LLM Idioms here blog.promptlayer.com/llm-idioms/
Claude reads our repos, analyzes every commit, and writes product updates grouped by feature. No more reading vague commit messages. Just clear, complete product emails without any user input. blog.promptlayer.com/how-i-automate…
Gpt-5-Codex is out. Here are our first reactions blog.promptlayer.com/gpt-5-codex-fi…
JSON prompting makes LLMs easier to validate. It also makes them think more like config files. Great for workflows and automation. Not great for style, nuance, or creativity. Use with intent. blog.promptlayer.com/is-json-prompt…
AI engineering interviews need a rethink. We look for experimental mindset and hands-on building experience over memorized best practices. The field changes too fast for textbook answers. blog.promptlayer.com/the-agentic-sy…
Too much context breaks the model’s ability to choose. The decision boundary softens. Outputs get slower, less useful, and more verbose. Clean prompts work better. Not because they’re short, but because they reduce noise. blog.promptlayer.com/why-llms-get-d…
Prompting is only one layer. The structure around it matters more. System messages, retrieval, metadata, memory, compression all of it shapes output. This is "context engineering" and it's where most real-world LLM products win or fail. blog.promptlayer.com/what-is-contex…
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