253 guides
Secure prompt engineering: keep untrusted text as data with message roles, delimiters, spotlighting, rules and output schemas. A real before and after.
LLM supply chain security as a checklist: inventory models, data and tools, pin and verify hashes, load safe formats, check provenance and plan fallbacks.
The LLM attack surface explained: user apps, integrations, APIs, data stores, insiders and supply chain, the common attack vectors, and the OWASP Top 10.
LLM agent orchestration patterns: single agent, chaining, routing, supervisor-workers and handoffs. When each fits and what the controller must enforce.
Build a ReAct agent from scratch in Python: the Thought, Action, Observation loop, a 60-line controller with a real trace, and how it maps to tool calling.
What prompt engineering is, how a prompt works, and the five parts of a good one, with a real before-and-after example and a map of the beginner path.
Fix the tool parameters array type must have items error: why VS Code and strict providers reject MCP tool schemas, and how to fix and test them.
SPIFFE vs AWS IAM roles vs Entra ID for AI agents: what an SVID is, how each gives an agent its own identity, and how to federate them, with runnable code.
Who decides when an AI agent is done? Stop conditions, self-checks, success criteria in code, a separate LLM judge and budgets as a backstop, with code.
The ReAct agent pattern explained simply: what an agent does after it calls a tool, a loop written by hand, the same agent in Promptise, where it breaks.
Policy as code for AI agent tool calls: when deterministic rules beat human approval or model judgment, and how to check each call with OPA before it runs.
LangChain multi-tenant isolation: what LangChain and LangGraph provide, what you build yourself, per-tenant rate limits and quotas, and the Promptise way.
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Each use case is a complete project with code and a guide. Pick one close to your problem and adapt it.