Towards Data Science
Prompt Engineering Isn’t Enough — I Built a Control Layer That Works in Production
✦TL;DR
Most LLM failures in production aren’t random — they’re predictable. I kept hitting broken JSON, silent failures, and outages that froze my entire app. Prompt engineering didn’t fix it. So I built a control layer above the model — and took structured output reliability from 0% to 100% without changi...
Want the full story? Read the original article.
Read on Towards Data Science ↗More like this
Integrating AWS API MCP Server with Amazon Quick using Amazon Bedrock AgentCore Runtime
AWS ML Blog•#agents
LLM Themes Are Not Observations
Towards Data Science•#llm
Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention
Ahead of AI•#llm
My Workflow for Understanding LLM Architectures
Ahead of AI•#llm
