grok-4 in promptlayer explaining what promptlayer is! what tasks are you updating from opus -> grok?

You can literally just hire a jazz band for the company office

Great report by the team at Chroma kelly These are my practical take-aways for prompt engineers (CONTEXT engineers): Your 100k token prompt is making your model dumber. even on tasks as simple as "repeat this string" too many tokens seriously degrade performance (we

Where does prompting go from here? Will AI swallow it all? Context engineering? Prompt engineering? This is our answer.

Huge congrats to the Humanloop team. They were one of the first in the evals space and a team I heavily respect. The platform is shutting down in September and we've received a lot of messages to move prompts & evals over to PromptLayer. Rolling out an official migration

Our elevator pitch: Write beautiful prompts Build reliable agents Create simple evals

"Context Engineering" is really growing on me most prompt issues are actually context issues. what you retrieve, keep, and drop shapes model behavior in production. it's not about clever instructions. It's about designing the environment around the model. wrote an article on

🛠️ Tool of the Day: PromptLayer What it does: → Tracks every GPT request + response → Lets you debug / compare / improve → Works with OpenAI + Claude + Groq etc. Why it matters: You can’t improve what you don’t track Bonus: → Auto-tags outputs that underperform →

The biggest mistake AI products make: Don't boil the ocean! Start simple. Get your AI product out in the wild. Start collecting data for evals. Most importantly: prove to your company the AI product should be invested in.

excited for this talk! see you at Betaworks soon

The shift from deterministic to assumption-based systems requires new technical architecture, and PromptLayer provides the infrastructure. Traditional systems need every parameter specified upfront. Modern AI systems use probabilistic defaults, contextual inference, and

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…

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…