Structured LLM Outputs & JSON Schema Extraction

Generating deterministic JSON Schemas from Pydantic models (model_json_schema) and enforcing constrained decoding or tool-calling modes in modern LLMs (OpenAI JSON Mode / Anthropic Tool Use).

Interactive Systems Visualizer

Pydantic v2 Core Rust Validation & Error Recovery

Raw LLM Output String:

{
  "query": "vector index latency",
  "confidence": 0.94,
  "is_urgent": true
}

Pydantic Core Parser Result:

STATUS: VALID

Parsed successfully by pydantic-core in 0.08ms

Key Systems Engineering Principles

Converts Python classes into OpenAPI-compatible JSON Schemas to constrain LLM sampling logits.
Guarantees output conforms to exact field names, nested objects, and enum constraints.
Forms the foundation of production Agentic tool use and reliable database query synthesis.

Mental Model Analogy

“Structured extraction is handing the model a pre-cut wooden puzzle frame: the model can only place tokens that physically fit into the designated shapes.”

Production Pitfall to Avoid

Asking the LLM to output valid JSON in the system prompt without passing the formal schema to the API runtime, resulting in markdown code-fence syntax errors (```json).

production_pattern.py

from pydantic import BaseModel
import json

class EntityExtraction(BaseModel):
    entities: list[str]
    is_urgent: bool

# Pass schema directly to model tool definitions:
schema = EntityExtraction.model_json_schema()
print(json.dumps(schema, indent=2))
# Model output string -> validated instance:
raw_llm_json = '{"entities": ["Server", "DB"], "is_urgent": true}'
parsed = EntityExtraction.model_validate_json(raw_llm_json)

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