Make LLM Systems Reliable: Build Trust Layers That Fail Safely
LLMs drift and “almost right” can quietly break code and data. This article shows how to make LLM systems reliable with validation, selection, fallbacks, and graceful failure.
LLM Prompt Engineering Best Practices: Clarity Under Constraint
LLM prompt engineering best practices treat prompts as input engineering—encode role, audience, constraints, structure, and tone to get precise, reliable outputs. A practical checklist for engineers.
Build Reliable LLM Systems: From Prompts to Production Reliability
Reliability in AI doesn’t come from clever prompts alone—it comes from the systems around them. Learn how to build reliable LLM systems with validation, caching, retries, and guardrails that meet cost and latency goals.
Guide AI with Constraints: A Guardrail Method for Reproducible CI/CD
Guide AI with constraints by stating invariants, hard limits, and acceptable tradeoffs so fixes honor contracts, not convenience. This principle-first method keeps CI/CD reproducible and advice trustworthy.
Evaluate AI Decision Quality: Build Reliable, Aligned Systems Beyond Prediction
Accuracy isn’t intelligence. This piece shows why to evaluate AI decision quality—shifting from prediction to aligned, agentic choices with human override, logging, and accountability.
From Wrapper to Moat: How to Turn AI Prototype into Business
Start simple—even with a wrapper—to validate demand fast, then layer moats through UX, domain workflows, integrations, and data before scaling. Build what users retain.
AI code security best practices start with one habit: ask AI to scan every change
When you code with AI, security must be continuous. Embed AI code security best practices by prompting for a vulnerability scan on every change to keep speed and resilience in sync.
Stop AI Generic Answers: From ‘Classic’ Labels to Concrete Debugging
Tired of seeing 'classic' from your assistant? Learn how to stop AI generic answers by insisting on evidence, context, and concrete next steps for faster, better debugging.
Operationalize AI in Workflows to Turn Data Into Decisions
A practical look at how to operationalize AI in workflows—building reliable data-to-decision pipelines that embed insights where work happens and measure decisions changed.
How to brainstorm with AI: sparks, constraints, and real-world creativity
Combine domains, add constraints, and revive old scraps—this guide shows how to brainstorm with AI to expand options and spark original, shippable ideas for engineers. Think sandbox sessions, pressure-tests, and a five-step mini-sprint.