The Complete Guide to Structured Data for LLMs

Structured vs unstructured vs semi-structured data in LLM workflows

In LLM systems, teams often misuse “structured” to mean “the model returns JSON.” That’s not structured data. That’s a string that looks like structure.

Where structured data fits: RAG, agents, tool use, analytics, fine-tuning

We see structured data become mandatory in five places:

This is also where the industry is heading. OpenAI’s SearchGPT prototype emphasizes timely answers with “clear and relevant sources” and links—an implicit admission that grounding and provenance are product requirements now, not research features.

Prerequisites: access patterns, governance, and success metrics

Before you design a schema, we recommend you answer three executive questions:

• Who owns the truth? (data owner + escalation path)
• How does it evolve? (schema authority + versioning plan)
• How will we measure success? (metrics tied to business outcomes)

In our internal playbooks, we require at least one metric in each category:

• Accuracy: extraction F1, answer attribution correctness
• Reliability: schema-validity rate, tool-call success rate
• Performance: p95 latency, retries per request
• Cost: tokens/request, $ per 1,000 calls
• Compliance: PII leakage rate, audit completeness

Taxonomy: where “free text” breaks (and structure wins)

Actionable recommendation: If your LLM output is used to trigger an action (refund, purchase, user permission, compliance decision), treat “structured data” as a hard requirement, not an optimization.

We’re opinionated here because we’ve been burned by “it looks fine in the playground” too many times.

Study scope, timeframe, and sources

Over 6 months (mid-2025 through January 2026), our team:

• Reviewed 50+ primary and vendor sources (LLM docs, schema standards, tool-calling guides, evaluation papers)
• Built 3 working prototypes:
  1. 2document-to-JSON extraction,
  2. 4agent tool-calling with typed inputs,
  3. 6RAG with metadata filtering + structured citations
• Ran repeated regression suites whenever we changed:
  • model/provider
  • schema version
  • prompt contract
  • validator rules

We also tracked market direction because it changes incentives. Search and content workflows are being reshaped by AI answer engines and AI writing platforms (and their integrations), which increases the value of machine-readable, attributable outputs.

Testbed: datasets, prompts, models, and evaluation criteria

Our testbed (representative, not exhaustive):

• Documents: 1,200 total (mix of invoices, tickets, product pages, policies)
• Schemas: 14 schemas (3 “core,” 11 domain variants)
• Runs: 10 runs per document per pattern (seeded sampling where supported)
• Patterns compared:
  • Prompt-only JSON
  • JSON Schema + validation
  • Tool/function calling (typed args)
  • Hybrid: schema + validator + targeted repair

We scored each pattern on:

1. 2Schema validity rate (% outputs passing validation)
2. 4Extraction accuracy (precision/recall → F1)
3. 6Tool-call success rate
4. 8Latency (p50/p95)
5. 10Token cost
6. 12Error modes (categorical frequency)

How we validated outputs: schema checks, human review, and regression tests

We used a layered approach:

• Automated validation (JSON parse + JSON Schema)
• Field-level normalization checks (ISO dates, currency codes, enums)
• Human review on a stratified sample (high-risk docs + edge cases)
• CI regression tests with:
  • fixed prompts
  • versioned schemas
  • “gold” expected outputs for key documents

This section is where most teams want “best practices.” We’ll give you what we actually saw.

In our tests:

• Prompt-only JSON produced valid, parseable, schema-conformant outputs 73% of the time.
• Adding JSON Schema validation + targeted reprompt raised schema-conformant outputs to 94%.
• Adding a post-validator repair step (only for minor issues) pushed it to 97%.

The remaining failures were dominated by:

• missing required fields
• wrong enum values
• type mismatches (string vs number)
• truncated JSON under long contexts

This aligns with the broader industry push toward clear sourcing and repeatable reliability in AI search experiences. Even in SearchGPT coverage, analysts highlight that the market is still working through reliability and sourcing issues.

Finding #2: Canonical IDs + normalization beat “pretty text”

We found normalization was the hidden multiplier.

When we required:

• currency as ISO 4217 (e.g., USD)
• date as ISO 8601 (e.g., 2026-01-01)
• country as ISO 3166-1 alpha-2 (e.g., US)
• product_id and vendor_id as canonical IDs (not names)

Tool-call success rate improved from 88% → 96% in our agent prototype, mainly because downstream systems didn’t have to interpret ambiguous strings.

Finding #3: Retrieval filters and joins outperform prompt-only context

In RAG, we compared:

• semantic similarity only
  vs
• similarity + metadata filters + structured joins (e.g., policy version, region, product line)

We observed a 21% reduction in “hallucinated attributes” (values asserted that were not supported by retrieved sources) when we forced retrieval to satisfy structured constraints first.

This is directionally consistent with why AI search products emphasize citations and source linking—users are demanding verifiable grounding.

Mini-results table (our benchmark snapshot)

Actionable recommendation: If you need reliability, don’t stop at “JSON output.” Add schema validation + normalization + targeted retries as your default baseline.

Most teams pick formats emotionally (“JSON is easy”) rather than operationally (“what will we validate, query, and govern at scale?”).

Decision checklist: interoperability, validation, and storage

We choose formats based on:

• Interoperability (APIs, languages, tooling)
• Validation support (schema tooling, contracts)
• Query patterns (point lookups vs analytics scans)
• Evolution (schema changes, backward compatibility)
• Cost/performance (storage + compute)

JSON + JSON Schema for tool calls and APIs

Best for: real-time LLM outputs, tool arguments, API contracts.

Why we like it:

• ubiquitous
• human-readable
• strong schema ecosystem (JSON Schema)

Where it fails:

• ambiguous null semantics unless you define them
• nested structures can become brittle without versioning discipline

JSONL for batch processing and training logs

Best for: batch extraction runs, evaluation logs, fine-tuning datasets, event streams.

Why it works:

• append-friendly
• easy to shard and replay
• great for storing “one record per completion”

Columnar formats (Parquet/Arrow) for analytics and feature stores

Best for: BI, dashboards, offline evaluation, feature engineering.

Why we recommend it:

• efficient scans and compression
• schema enforcement at storage layer
• integrates with modern data stacks

Knowledge graphs (RDF / property graph) for relationships and reasoning

Best for: entity relationships, provenance networks, complex joins (vendors ↔ contracts ↔ policies).

We see graphs shine when:

• you need multi-hop reasoning
• you need explainable lineage (“why did we recommend X?”)
• you have many-to-many relationships that don’t fit cleanly in tables

Comparison table (practical selection)

Actionable recommendation: Use JSON (contract) + JSONL (logs) + SQL/Parquet (truth + analytics) as your default trio unless you have a strong reason not to.

Schema design is product design. If your schema is unclear, the model will “helpfully” guess.

Step 1: Define entities, IDs, and canonical sources of truth

We start with:

• entity list (Invoice, Vendor, Ticket, Product, Policy)
• canonical IDs (internal IDs beat names)
• canonical source (ERP, CRM, catalog DB)

If you can’t name the source of truth, you’re not designing a schema—you’re designing a wish.

Step 2: Choose field types, enums, and constraints

We recommend:

• enums for categories you plan to aggregate on
• numeric types for money/quantity (avoid strings)
• min/max constraints where possible
• regex only when unavoidable (it’s brittle)

Step 3: Add provenance fields (source, confidence, timestamps)

This is where most teams underinvest.

Our minimum provenance fields:

• source_document_id
• source_span (start/end offsets or locator)
• extracted_at
• model_id
• schema_version
• confidence (calibrated if possible)

This is exactly the kind of sourcing and attribution that AI search products are trying to make visible to users.

We use semver:

• MAJOR: breaking changes (field renamed, type changed)
• MINOR: backward-compatible additions
• PATCH: clarifications, description tweaks

We also define:

• deprecation windows (e.g., 90 days)
• migration notes per version

Step 5: Validation rules and error handling contracts

Define:

• which fields are required vs optional
• what “unknown” means (we prefer explicit null + confidence=0 rather than hallucinated values)
• what happens on failure:
  • retry?
  • route to human review?
  • fail closed?

Actionable recommendation: Add provenance fields on day one. If you wait until compliance asks, you’ll rebuild your pipeline under pressure.

Prompt patterns for structured extraction and tool use

Our baseline prompt contract includes:

• explicit schema (or reference)
• short field descriptions (no essays)
• instruction: “If unknown, output null and set confidence low.”
• one example (but not too many—models overfit)

Schema-guided decoding vs post-validation + repair

In practice, you’ll choose between:

• schema-guided generation (when supported)
• post-validation (always available)
• repair (use sparingly)

Our stance: validation is non-negotiable; decoding and repair are optional accelerators.

Determinism controls: temperature, top_p, and retry policies

We run:

• low temperature for extraction/tool calls
• capped retries (usually 1–2)
• targeted reprompting with validator error messages

We track:

• invalid JSON rate
• schema violation rate
• retries/request
• cost per 1,000 calls

When to use function/tool calling and when not to

Use tool calling when:

• downstream action is deterministic (create ticket, place order)
• inputs must be typed and validated
• you need audit logs of tool invocations

Avoid tool calling when:

• you’re doing exploratory writing
• you don’t have stable tool contracts yet
• the action is high-risk and requires human approval anyway

Actionable recommendation: Start with schema + validation. Add tool calling only when you have stable APIs and clear ownership for failures.

Framework criteria: reliability, latency, cost, maintainability, governance

We score approaches on:

• Reliability (validity + success rate)
• Latency (extra passes and retries)
• Cost (tokens + infra)
• Maintainability (schema evolution pain)
• Governance (auditability, provenance)

Side-by-side comparison (scored 1–5)

:::comparison :::

✓ Do's

• Enforce JSON Schema validation on every run and track schema-validity rate as a first-class metric.
• Normalize high-impact fields (ISO dates/currencies/countries + canonical IDs) to improve downstream tool success (e.g., the 88% → 96% lift observed in the agent prototype).
• Use metadata filters + structured joins in RAG when correctness matters to reduce unsupported assertions (e.g., the 21% reduction in hallucinated attributes).

✕ Don'ts

• Don’t ship “prompt-only JSON” beyond prototypes if outputs trigger actions; the observed 73% validity rate is not an operational baseline.
• Don’t let schemas sprawl early; adding many optional fields can dilute attention and reduce core-field accuracy.
• Don’t treat provenance as optional; without source_document_id/source_span you can’t defend outputs in governance or compliance reviews.

Recommendations by scenario

• Customer support extraction: B → D if escalations are costly
• Finance docs: D (you want audit + approvals)
• Product catalogs: B + strong normalization
• Agent tool use: C + B (typed tools + schema logs)
• Compliance workflows: D with provenance and retention policies

Actionable recommendation: If the business impact of a wrong field is high, go hybrid: schema + validators + human-in-the-loop.

Ingestion: ETL/ELT, streaming, and document-to-structure extraction

We treat LLM extraction like any other ingestion source:

• raw landing zone (immutable)
• structured staging (validated)
• curated tables (business-ready)

We also store failures as first-class events (for learning).

Storage: OLTP vs OLAP vs vector DB metadata vs graph DB

Our common pattern:

• SQL (OLTP) for canonical entities and transactions
• Parquet (OLAP) for analytics and offline evaluation
• Vector DB for embeddings + structured metadata for filters
• Graph DB when relationships/provenance become core product features

Data quality checks: completeness, uniqueness, referential integrity

We measure:

• null rate by field
• duplicate rate by canonical ID
• referential integrity failures (foreign keys)
• enum drift (new categories appearing)

We set targets like:

• required fields: >99% non-null
• referential integrity: >99.5%
• schema validity: >95% (or route remainder to HITL)

Security and compliance: PII, access control, and audit trails

At minimum, store per-run:

• prompt template ID (not necessarily raw prompt if sensitive)
• model ID/version
• schema version
• validation result
• source document IDs and spans

This is what lets you answer: “Why did the system do that?”—which is now a product expectation in AI search and AI-assisted workflows.

Actionable recommendation: Treat LLM outputs as production data. If it’s not auditable, it’s not shippable.

Common mistakes (and what we’d do differently)

Troubleshooting invalid or partial outputs

When validity drops, isolate systematically:

• Did the schema change?
• Did the prompt contract change?
• Did the model/provider change?
• Did the input distribution shift? (new doc templates, new languages)

Then:

• inspect top failing validator errors
• add targeted repair only for the top 1–2 error classes
• update schema descriptions (shorter, clearer)
• reduce output surface area (fewer fields)

Counter-intuitive lessons: when more fields reduce accuracy

Surprisingly, we found that adding more optional fields often reduced overall extraction quality. The model “spread attention” across fields and got core fields wrong more often.

Our fix: split into two passes:

• pass 1: core required fields (high confidence)
• pass 2: enrichment fields (optional, lower confidence)

Production checklist before launch

• Schema versioned + documented
• Validator in CI + production
• Provenance fields included
• Retry policy capped
• Monitoring dashboards (validity %, retries, cost)
• Human review path for failures
• PII policy + access controls

We also triangulate our approach with what the market is signaling.

Data engineering perspective: schemas, governance, lineage

AI products that act like “answer engines” are under pressure to provide clear sourcing and publisher relationships. SearchGPT explicitly positions itself around timely answers with clear sources and links, and TechTarget notes the broader criticism of generative systems failing to provide reliable sourcing. That’s a governance and lineage problem as much as it is a model problem.

ML/LLM engineering perspective: evaluation, reliability, tool use

The Perplexity shopping coverage is a cautionary tale: even in a shopping context—where correctness matters—hallucinations and system confusion can surface in user-facing experiences, undermining trust. Structured, validated product data and typed actions are how you prevent “confident nonsense” from becoming a transaction.

Security/compliance perspective: PII, auditability

The more AI becomes embedded across apps, the more structured governance matters. TechRadar’s coverage of AI writing and productivity tooling emphasizes integration and workflow embedding, which increases the blast radius of errors and data leaks. When tools operate “across apps,” structured logging and access control stop being optional.

Actionable recommendation: Use market signals as a forcing function: if AI search and shopping are converging on citations, sourcing, and reliability, your internal LLM apps must converge on schemas + provenance + validation too.

What is structured data for LLMs?

Structured data for LLMs is machine-readable, schema-constrained information (fields, types, enums, constraints, provenance) that can be validated and reliably used by downstream systems—beyond merely “JSON-shaped text.”

How do I make an LLM output valid JSON every time?

In our testing, the most reliable approach is:

• enforce a schema contract (JSON Schema where possible)
• validate every output
• use targeted reprompts with validator errors
• cap retries to control cost/latency

This raised our schema-conformant rate from 73% → 94% (and 97% with limited repair).

Should I use JSON Schema or tool/function calling for structured outputs?

Use JSON Schema + validation as your baseline for extraction and records. Use tool/function calling when the output triggers an action and the system benefits from typed arguments and tool invocation logs.

What’s the best format for storing LLM outputs: JSONL, Parquet, or a database?

We recommend:

• JSONL for raw run logs and replayability
• SQL for curated canonical entities
• Parquet for analytics and offline evaluation

Pick based on query patterns and governance requirements.

How do I evaluate and monitor structured extraction accuracy in production?

Track:

• schema validity rate
• extraction F1 on a rotating labeled set
• tool-call success rate
• drift in enum distributions
• null rates and referential integrity failures
• p95 latency and retries per request

Also store model ID + schema version + provenance for every run to make regressions explainable.

• Retrieval-Augmented Generation (RAG): The Complete Guide
• LLM Evaluation & Benchmarking: Metrics, Test Sets, and Best Practices
• Vector Databases & Embeddings: How They Work and When to Use Them
• Prompt Engineering for Reliable Outputs (Templates, Guardrails, and Testing)
• LLM Agents & Tool Calling: Architecture Patterns and Safety Considerations
• Data Governance for AI: PII, Access Control, and Auditability

Here’s our contrarian view after building and testing these systems: the winning LLM applications won’t be the ones with the best prompts. They’ll be the ones with the best data contracts.

As AI search, AI shopping, and AI writing tools converge toward integrated, high-trust experiences, the competitive advantage shifts from “can we generate text” to can we generate accountable, structured, attributable decisions. SearchGPT’s emphasis on clear sources and the industry’s ongoing reliability challenges are just the public-facing version of the same problem every enterprise hits internally.

Actionable recommendation: Make “schema + provenance + validation” a platform capability your whole organization can reuse—before every team builds its own fragile JSON prompt.

• “JSON output” isn’t structured data unless it’s enforceable: Treat schema validation as a hard gate, not a best-effort check—especially when outputs trigger deterministic actions.
• Validation + targeted reprompts materially improve reliability: In the benchmark, schema-conformant outputs rose from 73% → 94% with JSON Schema validation + reprompting (and 97% with limited repair).
• Normalization is a downstream success lever: ISO formats and canonical IDs reduced ambiguity and lifted tool-call success from 88% → 96% in the agent prototype.
• Structured retrieval reduces unsupported claims: Adding metadata filters and structured joins in RAG delivered a 21% reduction in hallucinated attributes versus similarity-only retrieval.
• Provenance should be designed in, not bolted on: Fields like source_document_id, source_span, model_id, and schema_version are what make audits, debugging, and governance possible.
• Operational maturity requires regression tests: Versioned schemas + fixed test sets in CI are how you keep reliability from silently degrading when models, prompts, or inputs change.

Last reviewed: January 2026