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#Prompt Engineering

All articles tagged with “prompt engineering”.

LLM Prompt Guardrails for Error Handling

Split guardrails into input, output, and fallback: validate inputs, enforce JSON outputs, limit retries, and escalate high-risk cases.

Layer request-by-request checks across inputs, retrieval, outputs, and tool calls to prevent prompt injection and unsafe actions.

Treat all outside text as untrusted; separate instructions, lock keys, validate I/O, run red-teams, and have rollback ready.

Treat output moderation as a production control layer: define policy, layer detections, set category thresholds, and design safe fallbacks.

One API gateway routes prompts across LLMs, handling auth, model routing, retries, logging, cost control, and prompt versioning.

Treat prompts like releases: version typed blocks (role, context, instructions, guardrails, output) for quick rollback and auditable safety.

Runtime controls — not policies — make legal AI defensible: block inputs, verify outputs, route to human review, and log everything.

Test prompts on a golden set with 3–5 metrics (quality, cost, latency, safety), use paired tests or A/B, and enforce release gates before shipping.

Treat prompts as versioned config: lock variable contracts, validate inputs, separate drafts, check envs, and enable rollback.

Treat runtime prompt variables like typed, validated inputs to avoid missing fields, leaks, and formatting errors.

Keep prompt fronts identical to cut LLM API costs and latency—provider and app caching can halve input spend.

Test prompts against real user tasks: build a golden set, use structured outputs, run controlled A/B tests, and monitor regressions.