Defending the trust boundary in LLM apps: direct and indirect prompt-injection defense, input validation, schema-validated output, and PII redaction — with the anti-pattern named beside each safe one.
Guardrails for LLM Apps in Java
Defending the trust boundary in LLM apps: direct and indirect prompt-injection defense, input validation, schema-validated output, and PII redaction — with the anti-pattern named beside each safe one.
You’ve spent weeks optimizing your transformer-based model. You’ve pruned the weights, quantized the...
In the last post, we talked about 2012 — the year deep learning stopped being an academic curiosity...
A new GitHub repo enables AI coding agents to self-improve by recognizing effective coding paths, streamlining future development tasks for engineers.