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Practical guides to AI-driven development, full-stack architecture, and solving production problems.
Showing 85–96 of 100 articles
Handle slow and failed AI responses in a normal web application
Model slow AI requests as explicit application states with bounded timeouts, cancellation, request ownership, and recovery that preserves the user's work.
Build a document Q and A feature with verifiable citations
Build document Q&A around permission-aware retrieval and claim-level evidence so citations can be checked and unsupported questions produce a useful fallback.
Stop a retrieved document from becoming an instruction to your agent
Keep retrieved documents from controlling an AI agent by separating content from authority, enforcing server-side permissions, and testing malicious instruction-shaped text.
Return structured AI output without trusting its JSON
Validate AI generated JSON at both the shape and business-rule boundaries, then handle invalid, unsupported, and incomplete output without silently trusting it.
Build a small evaluation set before changing your AI prompt
Create a small representative evaluation set before editing an AI prompt so improvements, regressions, refusals, and ambiguous cases become visible and repeatable.
Choose between a normal function and an LLM for a product feature
Choose deterministic code or an LLM by separating exact product rules from language interpretation, then compare uncertainty, verification, latency, and operating cost.
Build an evidence checklist for AI assisted changes
Build a change evidence checklist that distinguishes observed behavior, executed checks, skipped boundaries, and remaining risk in AI assisted engineering work.
Detect when an AI assistant invented an API or dependency
Detect invented APIs in AI suggestions by checking installed package versions, exports, type declarations, and a minimal executable example before integrating new code.
Use AI to explain unfamiliar code without trusting invented architecture
Use AI to map unfamiliar code through concrete entry points, callers, and data flow, then verify its explanation without accepting invented architecture labels.
Write repository instructions that prevent repeated AI coding mistakes
Turn recurring AI coding review failures into short repository instructions with clear scope, concrete examples, and checks that remain useful as the project changes.
Keep AI assisted refactoring from becoming an accidental rewrite
Keep an AI assisted refactor behavior preserving by naming invariants, capturing representative outputs, and separating structural cleanup from product changes.
Make an AI coding task small enough to review and finish
Split AI coding work around observable outcomes and stable contracts so each change can be implemented, reviewed, and verified without expanding into a rewrite.
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