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Overview

The AI planner needs to know what blocks exist and what props they accept. Rather than sending raw Zod schemas or JSON Schema to the LLM, we compile block definitions into a contract format optimized for LLM comprehension and token efficiency.

Contract Pipeline

Layer 1: Zod Block Schemas

Each block type is defined in packages/shared/src/blocks/*.ts with a Zod schema and metadata:
Metadata includes listFields (array item shapes), imageSpecs (aspect ratios, dimensions), and field kind markers (e.g. richtext).

Layer 2: Hand-Written Notes

_blockNotes in apps/orchestrator/src/nlp/deterministic-planner-suggestions.ts provides behavioral guidance that Zod schemas cannot express:

Layer 3: Contract Assembly

blockContractsSummary() walks each registered block’s Zod schema and produces:
When _blockNotes has no entry for a block type, the builder auto-derives notes from metadata (array item shapes, image specs, field kinds).

Layer 4: Adaptive Budgeting

buildPlannerSchemaContext() in apps/orchestrator/src/chat/planner.ts selects which contracts to include based on intent: Budget is capped at CHAT_SCHEMA_BUDGET_BYTES (default 9000). If the preferred mode exceeds the budget, it falls back: full → targeted → minimal.

Comparison with Puck AI’s Approach

Puck AI uses field-level JSON Schema co-located with each field’s UI config:
Side-by-side:

Why Not JSON Schema

We evaluated switching from prop-list contracts to auto-generated JSON Schema (via z.toJSONSchema() from our Zod definitions). The analysis identified several downsides:

1. Behavioral notes cannot be expressed in JSON Schema

The most valuable part of our contracts is the hand-written guidance. JSON Schema has no equivalent for: Switching means either losing this guidance (more hallucination) or keeping notes alongside JSON Schema (duplicating info, higher token cost).

2. JSON Schema is more verbose

Hero contract today: ~1,124 bytes. Equivalent JSON Schema with properties, type, enum, items, required: ~1,400-1,600 bytes (25-40% larger for structure alone, before behavioral guidance). At 20 blocks, the full payload grows from ~10.7 KB to ~14+ KB — well past the 9 KB budget. The adaptive fallback triggers more aggressively, degrading more requests to “targeted” or “minimal” mode.

3. JSON Schema expressiveness doesn’t help LLMs

Features like minItems, pattern, exclusiveMaximum, if/then are designed for machine validators, not LLM comprehension. LLMs respond better to natural language (“columns must be ‘2’, ‘3’, or ‘4’”) than to {"enum": ["2","3","4"]}.

4. Structured outputs don’t need per-field JSON Schema

OpenAI structured outputs (response_format) need JSON Schema for the response shape (EditPlan), not for block prop schemas. We already support this via CHAT_STRICT_JSON_RESPONSE. Block prop values are freeform Record<string, unknown> — constraining them via response_format would require a discriminated union of all block types, which is fragile and explodes schema size.

5. Auto-derivation already handles simple cases

blockContractsSummary() auto-derives notes from Zod metadata when _blockNotes has no entry. Hand-written notes exist precisely for cases where auto-derivation isn’t enough.

6. External blocks already use JSON Schema

For manifest-only blocks from external sites (no Zod schemas), the contract builder already derives contracts from JSON Schema. So we get JSON Schema benefits where they matter without paying the cost for our own blocks.

Where JSON Schema Would Help

The main opportunity is adding more auto-derived note patterns to reduce the hand-written surface:
  • Auto-generate richtext convention docs from field kind: "richtext"
  • Auto-generate enum default guidance from z.enum().default()
  • Auto-generate cross-field dependency hints from related optional fields
This would keep the current format while reducing manual maintenance — better than replacing the format entirely.