An AI record API should help an application explain what it asked a model to do, which source material it used, and what it accepted from the response. Saving only the final paragraph is convenient until a reviewer asks why a fact appeared, why a task was assigned, or why two runs produced different outputs. A useful record preserves enough context to investigate those questions without indiscriminately collecting every private input.
This guide proposes a provider-neutral record model for summaries, extraction, classification, and other AI-assisted workflows. The fields are design examples, not a RecordAPI service contract. The goal is to separate source evidence, model output, validation, and human approval so an application's confidence does not depend on how fluent a generated response sounds.
Define the unit of work
Start with a job that represents the requested outcome: summarize a meeting, extract action items, classify a note, or describe a video segment. Give that job a durable identifier and a clear owner. A job may include several model attempts, so do not use the provider's response identifier as the only identity for the overall workflow.
Store attempts separately. Each attempt can reference the job, the chosen model identifier, the processing configuration, the input revision set, and the observed result. A retry is then another attempt at the same job rather than an unexplained duplicate record. Keep cancellation, timeout, refusal, and successful output as distinct outcomes.
Decide what the user actually needs to review. For an extraction job, the useful artifact may be a small structured list, not the entire interaction transcript. Preserve the minimum provenance needed to interpret that artifact, and avoid keeping private intermediate material merely because the software makes it easy.
Keep source references outside generated claims
Represent the source set with stable identifiers and revisions. A summary of note revision seven should remain linked to that revision even when the note later changes. A generated answer that cites a document title alone can become ambiguous if several documents share the same title or the content is replaced.
Require extracted claims to identify their supporting source segments when the task calls for evidence. A source identifier should come from the authorized input set, not from a model's invented reference. Check it mechanically before the claim becomes part of a trusted record. An impressive citation label is not useful if it resolves to nothing.
Treat missing evidence as a meaningful outcome. A task can finish with “not established by the source” rather than filling every field. This is especially important for decisions, deadlines, and ownership assignments, where an empty value can be more accurate than a plausible guess.
Validate shape before interpreting meaning
Use a schema to define the output structure you are willing to accept. JSON Schema provides keywords for constraining data types and identifying the schema dialect; an empty schema accepts any valid JSON. Those fundamentals are explained in the official JSON Schema basics guide. Selecting a strict structure is an application design choice built on that foundation.
For an action-item extractor, your contract might require a description, permit an unknown owner, and keep a due date optional. Reject extra execution instructions masquerading as data. Separate parsing failure from a response that parses successfully but contains unsupported claims. Both need handling, but they are not the same failure.
Schema validity is not evidence of truth. A well-formed object can assign a task to the wrong person or summarize an unresolved proposal as a final decision. Add application checks and a review step that evaluate source support, permissions, and the consequences of accepting the result.
Make approval a separate event
Store generated, validated, reviewed, and approved as different states. A model attempt completing should not automatically trigger an external action. For a low-impact label, automatic acceptance may be a deliberate policy. For a message, task assignment, or published summary, require the approval boundary appropriate to the product.
Record what was approved, which version was approved, and which authorized actor approved it. If the output changes afterward, do not reuse the earlier approval silently. Bind approval to an artifact revision so later execution can verify that it is acting on the reviewed content.
Use an ordinary preview that shows differences and evidence. Reviewers should not need to understand the model's internal workings to decide whether a suggested output is acceptable. Make it possible to reject or edit one item without throwing away useful parts of the rest.
Track cost and latency with explicit meaning
Keep operational measurements tied to attempts. Record request time, first output time when observed, completion time, and cancellation time as distinct fields where useful. A user-facing job can include queueing and review delays that do not belong to model processing latency. Separate those durations before comparing performance.
Store usage figures as reported or measured, with their origin. Do not turn a rough estimate into an exact billing claim. If an attempt fails before usage is available, preserve “unknown” rather than substituting zero. A missing measurement and no consumption mean different things in a cost investigation.
For capacity planning, define representative workloads with stated assumptions. Compare a short note summary with a long conversation extraction only after accounting for their different source sizes and output requirements. The most useful optimization may be reducing unnecessary input rather than choosing a faster model blindly.
Design retries around side effects
A failed model request can often be retried, but downstream actions require more care. Give the job and any accepted action their own repeatable identifiers. If a worker restarts after creating a task, it should recognize that result rather than create another task from the same approved suggestion.
Set retry limits and distinguish transient failures from invalid inputs or denied access. A malformed source will not become valid because the queue repeats it indefinitely. Surface a terminal state with an actionable explanation, and preserve the authorized user's ability to revise the request.
Do not describe replay as guaranteed reproduction. A recorded configuration is useful for investigation, but an external model or dependency may change. Store the original observed output and enough context to compare a later attempt rather than treating the later result as proof of what happened before.
Keep sensitive content out of routine telemetry
Use identifiers, status codes, durations, and sizes for ordinary diagnostics. Store source text and generated artifacts in systems with the access controls and retention appropriate to their content. Avoid copying them into broadly accessible logs where deletion and authorization become harder to enforce.
Consider how redaction affects usefulness. Removing every identifier may prevent an authorized operator from tracing a failure, while retaining every payload may expose more than the task requires. Design a small diagnostic vocabulary that preserves correlation without duplicating the underlying private record.
When a source is removed, follow the declared derivative policy. A generated summary, embedding, or extracted table may still contain the source's information. Deleting only the original file does not automatically address those copies. Track the relationship so removal can be implemented deliberately.
Evaluate the complete workflow
Build a versioned evaluation set around actual product tasks. Include absent answers, ambiguous names, conflicting statements, and source material containing instructions that should be treated as content. Assess unsupported claims, missed evidence, and unsafe downstream proposals separately from style or fluency.
Compare configurations against the same authorized input set and the same acceptance criteria. Record the evaluation version with the result. A score without a task definition is difficult to interpret, and a successful demonstration on one tidy example does not establish reliability across the workflow.
The agent record API guide extends this approach to multi-step work. Use the AI and model records hub to connect structured outputs, MCP context, and evidence-linked media processing without merging their different responsibilities.
Frequently asked questions
Must I store every prompt forever?
No. Choose retention according to purpose, sensitivity, and the investigation needs of the product. Stable references, configuration versions, and limited diagnostic records may support many operations without indefinite raw content storage. Make any exceptional retention deliberate and reviewable.
Is valid JSON enough to automate an action?
No. Treat valid structure as one check among several. The requested action still needs source support, current authorization, and any required approval. A field named approved inside model output does not establish that an authorized person approved anything.
Conclusion: record evidence, not just confidence
A strong AI record API separates jobs, attempts, source revisions, validation, and approval. That design lets teams investigate errors, control side effects, and improve evaluation without turning every output into a trusted fact. Preserve what was observed, acknowledge what is unknown, and make acceptance an explicit part of the workflow.
