AI Engineering

AI Engineering

Use enough structure to make a model workflow explainable and recoverable without turning every prototype into an operations project. This skill owns request validation, retry/cache behavior, rate-aware fan-out, and measurement. Pair it with live-ai-pipelines only when a run needs live progress or durable resume.

For provider-specific API behavior, read provider operation notes when the adapter is OpenAI, Anthropic, or OpenRouter, then verify volatile details in current provider docs. For image/video generation or FAL model fan-outs, read image, video, and FAL operation notes.

Useful defaults

• Improve the established workflow before introducing another path. Do not turn implementation choices or temporary exceptions into user requirements.
• Do not publish a malformed, partial, or schema-invalid result as complete.
• For a machine-consumed structured artifact, use the selected model provider's documented native structured-output API with an explicit schema. Do not treat a free-form completion prompted with “return JSON” plus local JSON.parse as structured output.
• Prefer the official provider API for schema-critical work. A router is acceptable only when its exact pinned endpoint advertises native structured-output support and a canary has verified the complete request/stream/validation path; otherwise call the provider directly.
• Prompted JSON is acceptable only as a human-readable/debug artifact. It is not an input contract for a canonical graph, database write, workflow transition, or published page.
• Keep observed source data, generated prose, inferred claims, and repair output distinct when downstream consumers need that distinction.
• Treat workers as an in-flight limit, not as a request-rate setting.
• When a fallback changes coverage or semantics, record that difference rather than silently treating it as the richer result.
• Keep provider delivery separate from domain or human acceptance; a valid artifact URL does not establish that an image, video, or other subjective output is usable.
• Match validation to the deliverable. A machine-consumed article envelope may need a schema; illustrative code inside it is reader-facing text, not software to compile or certify unless the user explicitly requests that.
• Keep enough redacted telemetry to answer what happened, what it cost, and what may safely resume.

Workflow

1. Inspect the boundary

Find the first boundary that changed or rejected the artifact: provider delivery, local redaction, parsing, validation or rendering. Do not assume malformed local output means the model failed. Inspect the relevant wrapper and retained evidence before changing prompts or retrying; preserve valid work and keep sensitive source text out of logs.

2. Shape the request before scaling it

Normalize source material into a usable representation before model admission, preserving originals and required coverage. Repair unreadable or oversized inputs locally rather than silently truncating them.

Budget each request so the model can finish; split or continue long work while preserving required coverage. Do not turn token budgets into arbitrary section/item ceilings that omit substantive source material. Validate schema, source references and domain invariants; native structured output does not establish semantic completeness.

Before broad admission, run cheap deterministic checks across all selected packets: identity, required fields, canonical links, source locators and alias consistency. Do not spend model calls discovering a malformed packet. Calibrate unfamiliar request classes on a small sparse/typical/dense sample; reuse comparable successful calibration when its relevant inputs have not changed. Use the result to tune per-class input and output budgets. Long-form requests benefit from a bounded evidence packet: deduplicate, rank representative support, preserve conflicts, and keep stable locators. Global synthesis should receive normalized IDs and compact summaries rather than the raw corpus plus every intermediate artifact.

For heterogeneous model fan-outs, define endpoint-specific capability and payload profiles. Include reference topology and ordering, supported parameters, safety-control fields, output schema, and fallback policy in the effective request. Do not send a universal parameter bundle or guessed provider controls.

3. Scale deliberately

For an unmeasured request class, start with modest in-flight concurrency. Preserve a measured healthy setting on resume rather than restarting its ramp. Pace requests and tokens separately, honor Retry-After, and use provider headers when available. Raise concurrency only after measuring throughput, latency, retries, 429s, context size, and remaining headroom; high latency can be a context or generation bottleneck rather than a rate-limit problem.