prompt-engineer

To prevent prompt injection, design system prompts using strict template isolation, instruction grounding, and data-only boundaries for untrusted content. This approach separates fixed instructions from variable data, ensuring external inputs cannot override the agent's core behavior.

Ensuring deterministic JSON outputs requires specifying strict input/output schemas with explicit status and error codes. By defining few-shot structured examples and deterministic failure handling, downstream agents can safely chain together without parsing errors.

Creating multi-agent orchestration contracts involves defining sub-agent roles, routing logic, aggregation contracts, and error propagation rules. This structured approach prevents pipeline breaks by ensuring explicit I/O schemas and least-privilege scope limits across agents.

Inconsistent structured outputs often result from missing explicit output schemas, insufficient few-shot examples, or lack of deterministic failure handling. Applying a quality checklist for variable typing and schema correctness standardizes the prompt template behavior.

Yes, prompt engineering applies to both single-agent and multi-agent workflows. It involves designing system prompts, prompt templates, and orchestration contracts with least-privilege scope limits and structured I/O schemas across single-agent and multi-agent pipelines.

You need explicit I/O contract design for AI agents when chaining pipelines, routing tasks, or defining skills. Strict schemas with status/error handling ensure downstream agents parse outputs safely and handle failures deterministically.