AI chat table output

AI chat table output with native Ace Grid results

Internal AI chats increasingly return citations, lists, tables, and actions. Ace Grid adds another output shape: a validated, interactive grid that users can sort, filter, edit, select, inspect, and audit.

Open AI Suite docs See AI output guide

Choose a grid only for multi-record work

A short factual answer or small read-only table should remain prose or markdown. Use Ace Grid output when the assistant returns enough business records that users need sorting, filtering, selection, editing, validation, comparison, or approval. Account risk, support triage, finance review, compliance checks, and operations queues are strong candidates because the answer becomes a temporary workspace rather than a static message.

Define a versioned assistant contract

The model should return a constrained document rather than arbitrary React props. Include schema version, typed columns, stable row IDs, values, provenance, validation issues, and an allow-listed command set. Versioning lets the application reject or migrate incompatible output. Unknown privileged fields should fail validation instead of being ignored. The contract should be understandable without depending on a specific model provider.

Preserve provenance with the rows

Store source labels, URLs, timestamps, model or provider information, confidence where meaningful, and warnings. Provenance must remain accessible when users sort or filter the result. A valid schema proves only that the output has the expected shape; it does not prove that values are current or correct. The application should verify sensitive facts against authoritative systems before users act on them.

Separate rendering from commands

Rendering validated rows is lower risk than changing business state. Suggested commands should reference allow-listed operations and stable row IDs, never executable code. Before applying a command, the server rechecks authorization, current record versions, and business rules. Consequential changes require explicit user approval. Return per-row outcomes so the grid can show success, rejection, conflict, or retry without hiding partial failure.

Design failure and fallback states

Handle streaming, incomplete output, schema errors, missing citations, empty results, expired authorization, and backend failures. Preserve a readable text fallback and the raw response for diagnosis without exposing sensitive internals to unauthorized users. Do not silently coerce malformed fields into valid-looking rows. Explain which part failed and let users retry the assistant request or continue with the verified portion.

Protect sensitive data

The assistant, retrieval layer, grid, logs, and mutation service must enforce the same data-access rules. Limit fields before model invocation, avoid placing sensitive prompts or payloads in client logs, and redact diagnostics according to policy. Revalidate authorization when a user applies an action because permissions or record state may have changed since generation. A schema does not replace security controls.

Product evidence

Schema validation before mutation

@ace-grid/schema-ai exports createGridAiResultSchema, validateGridSchemaDocument, and applyGridSchemaDocument. Generated grid documents can be validated before actions mutate grid state.

Structured AI contracts

The package includes result schemas for render, update, and command workflows, chat conversation schemas, and JSON schemas for model providers or local inference tools.

Live Ace Grid example

Native AI chat table output

An assistant response becomes a validated, interactive Ace Grid block with business rows and auditable actions.

Chat outputResult typeValidationScoreTrustAction
Chat promptStructuredLive9696%Validate
Grid commandActionReview9191%Apply
AI tableInteractiveLive8888%Render
Audit trailGovernedEscalate7474%Approve

Validated AI grid output example

import { Grid } from "@ace-grid/enterprise";
import {
  createGridAiResultSchema,
  validateGridSchemaDocument,
  applyGridSchemaDocument,
} from "@ace-grid/schema-ai";

const resultSchema = createGridAiResultSchema({
  kind: "update_view",
  view: assistantGridView,
});

export function applyAssistantResult(actions) {
  const validation = validateGridSchemaDocument(resultSchema);
  if (!validation.valid) return validation;

  return applyGridSchemaDocument({ actions, value: resultSchema });
}

export function AssistantAnswerGrid({ rows, columns }) {
  return <Grid data={{ rows, columns }} columns={{ columnWidths: {} }} layout={{ width: 1200, height: 420 }} />;
}

Limitations and tradeoffs

  • Keep a short answer as text or a simple table when users do not need to sort, inspect, select, or act on multiple records.
  • Do not expose generated mutation commands without schema validation, explicit approval, and server-side authorization.

Common questions

When is a grid better than a markdown table in chat?

Use a grid when the answer contains enough records that filtering, sorting, selection, editing, provenance, or follow-up actions improve the task.

Can a validated AI result update records directly?

Validation confirms shape, not permission or truth. Send approved actions to the server for authorization, version checks, and business validation.

Sources