Turn a recurring AI task into a tested, bounded workflow

Write the job as an input-to-decision contract

Choose one repeated task such as turning a verified source packet into a draft outline. Name the user, starting state, allowed inputs, desired decision or artifact, excluded work, stakes, owner, and stop state. Separate drafting from publishing or external action. Capture the current manual baseline and defects before introducing AI. The AI workflow build sheet begins with a testable job, not a persona such as world-class expert. If a human cannot agree on what counts as complete, prompt iteration will optimize conflicting expectations. Mark claims requiring sources, private data that must stay out, and decisions requiring qualified review.

Evidence: Anthropic; National Institute of Standards and Technology

Build a compact evaluation set from real variation

Create cases for a normal input, missing context, contradictory sources, malformed document, prohibited data, ambiguous instruction, adversarial external text, and a situation that should escalate. Write expected properties rather than one exact ideal paragraph: required sections, citations linked to provided sources, explicit uncertainty, no invented metrics, and no external publication. Define automatic schema checks and a human rubric with examples of pass, revise, and stop. Keep a holdout set to catch overfitting. Record baseline behavior before changes. Success means improvement across the set within cost and latency limits, not a single favorite response.

Evidence: OpenAI; Anthropic

Assemble context with labels, provenance, and a budget

Provide only material needed for the task. Separate governing instructions, verified references, user text, retrieved candidates, and untrusted content. Attach identifiers, dates, access boundaries, and permitted use. Summarize long context only when the summary itself is verified against the source, and keep a path to original passages. Decide what happens when context exceeds limits: retrieve narrower sections, ask the user, split the job, or stop. Do not let recent conversation silently override durable requirements. Version the context assembly so an evaluator can distinguish a prompt improvement from a different source set.

Evidence: National Institute of Standards and Technology; Vanderbilt University researchers

Write instructions around decisions and failure behavior

State role only when it clarifies responsibility; then specify task sequence, evidence rules, prohibited inferences, source-use method, output contract, uncertainty, and escalation. Use examples for genuinely difficult categories, including a negative example and why it fails. Avoid contradictory commands such as be exhaustive and be brief without priority. Tell the model what to do when a source is absent rather than merely saying do not hallucinate. OpenAI, Anthropic, and Vanderbilt sources offer techniques and patterns, but syntax and model response differ. Test instructions empirically on the selected provider and model. Keep prose readable enough that a reviewer can see what changed.

Evidence: OpenAI; Vanderbilt University researchers

Constrain tools and validate structured handoffs

Expose the smallest necessary tool set with narrow parameters, read-only defaults, scoped credentials, time and call limits, and no secrets in descriptions. Validate tool arguments, permission, and result type outside the model. For software handoffs, define a schema with required fields, enums, maximum lengths, source IDs, and a review-required state. Reject or repair invalid output before downstream use. Add idempotency to reversible writes and require explicit approval for communications, spending, destructive actions, or privileged changes. Prompt language can explain the boundary; orchestration must enforce it. Log tool names and outcomes while redacting sensitive content.

Evidence: OpenAI; National Institute of Standards and Technology

Rehearse failure, freeze a version, and release narrowly

Run the full set, inspect regressions, simulate timeout, provider error, duplicate event, bad retrieval, tool refusal, and human rejection. Verify cancel, retry ceiling, rollback, and manual continuation. The next action is to complete one build sheet and release the workflow to a small internal task queue with no external publishing authority. Preserve prompt, model, context rules, tool schema, evaluator, results, owner, and review date as one version. Limits remain: evaluations approximate reality, models and vendors change, hidden attacks exist, and human rubrics can drift. Reopen design when a new failure class appears. A tool purchase cannot replace these controls; affiliate-linked providers remain subject to the same test evidence and disclosure.