59 articles tagged #Prompt Engineering
System prompts define how AI assistants behave, but they aren't always as secret as developers assume. Learn what system prompt leakage is, why it happens, the risks it creates, and how to build AI applications that remain secure even if prompts become exposed.
Function calling enables LLM agents to interact with APIs, databases, and external tools, but poorly formed arguments can cause unexpected failures. Learn why tool calls break, common causes of invalid parameters, and proven techniques for building reliable AI agents.
Bigger context windows don't automatically produce better AI responses. In many cases, stuffing an LLM with excessive conversation history, documents, or retrieved passages reduces answer quality, increases latency, and introduces distractions. Learn why context window bloat occurs and how to keep
Large Language Models don't hallucinate randomly. Certain categories of questions consistently produce higher rates of fabricated facts, nonexistent citations, invented APIs, and incorrect reasoning. Understanding these hallucination hotspots helps developers design safer AI applications
ChatGPT can sketch circuit breaker logic in seconds, but the code it produces often flaps — tripping and recovering so fast the pattern becomes useless. Here's how to prompt your way to production-grade breaker logic.
LLM agents often perform well during the first few interactions but begin forgetting important details as conversations grow longer. This phenomenon—often called multi-turn memory collapse—is usually caused by context limitations, summarization strategies, retrieval failures, or prompt design
ChatGPT-generated idempotency key logic often looks correct but silently breaks under retries, race conditions, or partial failures — costing you real money. Here's how to prompt it properly.
ChatGPT can sketch rate limit headers in seconds, but it routinely leaves out retry logic, resets timestamps, and client-side parsing — gaps that only show up when clients start hammering your API in production. Here's how to close them.
ChatGPT can scaffold RBAC middleware fast, but generic prompts produce generic code — and in access control, generic means gaps. Here's how to prompt your way to a production-ready implementation that won't leak permissions.
ChatGPT-generated seed scripts look clean until they hit your database and explode with foreign key errors, duplicate PKs, or null constraint failures. Here's how to prompt your way to seed data that actually inserts.
ChatGPT can draft Server-Sent Events handlers in minutes, but the output often silently drops events on reconnect or mishandles buffering. Here's how to prompt your way to production-grade SSE code.
ChatGPT can suggest useful database indexes, but it routinely misses compound index opportunities and column ordering rules. Here's how to prompt it correctly and catch the gaps before they hit production.