Schema Designer & Multi-Data Pipelines for LLMs

By Gwylym Owen — 18–24 min read

Executive Summary

AethergenPlatform can unify multi‑domain data—tables, events, documents—into LLM‑ready schemas and pipelines. Design once, then scale generation and training from millions to billions of records with governance and evidence you can trust as of September 2025.

Design Goals

These are the targets we’re hitting with a smile:

• Clear Schemas: Typed fields across domains with constraints and vocabularies—keep it neat!
• Composable Pipelines: Generate, validate, and package training corpora like a well-oiled machine.
• Evidence: Prove the data fits your tasks/models—no blind leaps here!

Schema Designer

Let’s build something beautiful with these tools:

• Visual & Code Views: Drag-and-drop entities and relations or code it up (e.g., YAML)—your choice, artist!
• Vocabulary Management: Curate lists (e.g., CPT codes) with versioned dictionaries—keep it fresh!
• Validation Rules: Range checks (e.g., age 0-120), regex (e.g., email format), and referential integrity (e.g., patient IDs link).
• Collaboration: Team edits with lock-in for breaking changes—smooth teamwork!

Multi-Data Pipelines

Pipelines covered:

• Structured: Tables and events with Delta/Parquet outputs—crisp and clean!
• Semi-Structured: JSON/Avro with schema evolution (e.g., add a field, auto-migrate)—flexible flow!
• Unstructured: Documents and images with annotations (e.g., spans) and embeddings (e.g., BERT vectors)—creative chaos tamed!
• Joins & Normalisation: Stitch it all together (e.g., claims to notes) with smart deduplication.

LLM Training Flows

Let’s train those LLMs with some flair:

• Instruction Tuning: Curated prompts (e.g., “Extract code”) with adapters for quick learning.
• Domain Adaptation: Synthetic augmentations (e.g., rare disease cases) to fit your niche.
• Evaluation Suites: Tasks like extraction, reasoning, QA with metrics (e.g., F1, accuracy)—test the wits!
• Robustness: Noise or OCR tests to see if it holds up under pressure.

Evidence

Here’s the proof to back it up:

• Data Quality: Metrics and coverage reports (e.g., 95% vocab hit rate)—no gaps!
• Task Performance: Results at fixed operating points (e.g., 0.75 F1 at 1% error).
• Ablations: Which features or augmentations shine (e.g., +5% with embeddings)—dig into it!

Scaling

Let’s scale it up without breaking a sweat:

• Sharded Generation: Split the work across nodes, checkpoint validation—keep it rolling!
• Device-Aware: INT8/FP16 training with quota controls (e.g., 30W GPU cap)—fit your hardware!
• Packaging: To MLflow/ONNX/GGUF with device profiles (e.g., Jetson settings)—ready to deploy!

Use Case Example

Scenario: A team blended clinical notes, claims tables, and device logs.

• Move: Harmonised with the schema designer, trained extraction and reasoning models.
• Result: Evidence showed a 19% F1 lift vs baseline at fixed error budgets, stable across facilities.
• Win: Procurement signed off in a week—high-fives all around!

Case Study

Scenario: A development team tested AethergenPlatform with a simulated retail dataset, combining synthetic sales events and customer feedback.

• Move: Used pipelines to integrate data, applied synthetic augmentations for variety.
• Result: Achieved a simulated 0.78 accuracy at a fixed operating point, with evidence validating coverage and quality.
• Win: Demonstrated pipeline scalability in a controlled environment, paving the way for real-world testing.

FAQ

Can we import existing schemas?

Yes—SQL/JSON schemas, plus inference from sample corpora.

How do we manage schema drift?

Versioned schemas and automated diffs; evidence highlights impacted tasks—stay on track!

Glossary

• Vocabulary: Controlled list of allowed values (e.g., CPT codes)—keeps it legit!
• Adapter: Lightweight tuning layer for LLMs—saves compute!
• Operating Point: Threshold aligned to business cost/benefit—your sweet spot!

Checklist

• Define Tasks: Set success metrics (e.g., F1 > 0.75)—know the goal!
• Design Schemas: Add constraints and vocabularies—build it right!
• Build Pipelines: Run evidence, package models—deliver with confidence!

Schema Example

entity Patient { id: string, age: int, region: enum[NA,EU,APAC] }
entity Note { id: string, patient_id: ref Patient.id, ts: datetime, text: string }
entity Claim { id: string, patient_id: ref Patient.id, code: string, amount: decimal }
relation R1: Patient 1..* Note
relation R2: Patient 1..* Claim
constraints: Claim.amount >= 0, Note.text nonempty
vocab: Claim.code in CPT_dict_v12
  

Validation Rules

• Range Checks: Amount, age; regex for codes; referential integrity—dot the i’s!
• Coverage Targets: Rare vocab entries (e.g., 90% hit rate)—catch the oddballs!
• Segment Balance: Even splits for training/evaluation—fair play!

Pipeline DAG

seed_ingest → schema_normalise → joins → augmentation → validation → packaging
                                   ↘ evidence_metrics ↗
  

LLM Data Cards

• Task: Extraction, reasoning, QA; datasets and splits listed—clear as day!
• Limits: Bias notes and refresh cadence (e.g., quarterly)—know the edges!
• Use: Intended use and out-of-scope behaviors—set expectations!

Evaluation Suites

• Extraction: F1 at fixed error budgets (e.g., 0.75 at 1% error).
• Reasoning: Accuracy on templated and free-form prompts—think it through!
• Robustness: Noise/OCR corruptions where relevant—toughen it up!

Security & Governance

• Lineage: Tracked from seeds to packaged corpora—trace it back!
• SBOM: Tools and artifacts signed—proof of origin!
• Access: Grants aligned to Unity Catalog roles—secure access!

SOP

1. Define Tasks: Set KPIs; draft schema—plan the journey!
2. Small Run: Generate, validate, iterate—test the waters!
3. Scale Up: Compute evidence, package—go big!
4. Train & Release: Evaluate, attach evidence, ship it—mission complete!

Appendix: Prompt Template Snippet

Given the schema and vocabularies, extract (code, amount) from the note.
Return JSON: {"code": "...", "amount": 0.0}
  

Schema Governance

• Visibility: Labels per field; masking where required—privacy first!
• Version Diffs: Automated migration notes—smooth updates!
• Approval: Workflow for breaking changes—team consensus!

Vocabulary Catalog

• Controlled Lists: CPT, ICD with region overlays—global fit!
• Deprecation: Windows and replacement guidance—plan ahead!
• Coverage: Dashboards for rare entries (e.g., < 5% misses)—track it!

Pipelines

ingest → normalise → join → annotate → validate → package → catalog
                         ↘ evidence ↗
  

Annotations & Embeddings

• Spans: Annotations for extraction; quality audits—spot the errors!
• Embeddings: Retrieval indices packaged—search-ready!

Training Flows

• Instruction Tuning: Adapters with OP evaluation—teach smart!
• Domain Adaptation: Synthetic augmentations with limits noted—fit the niche!
• Robustness: Noise/OCR checks where relevant—battle-tested!

Evidence & Cards

• Data Quality: Metrics and coverage by vocab/segment—full picture!
• Task Performance: OP results with CIs (e.g., 0.75 [0.73, 0.77])—solid stats!
• Limits: Intended use, refresh cadence—set the boundaries!

Scaling

• Sharded Generation: Distributed validation—scale without strain!
• Quota Controls: Device-aware batching—fit your setup!
• Packaging: MLflow/ONNX/GGUF with profiles—deploy anywhere!

Security & Governance

• Lineage: Seeds to corpora; SBOM for tooling—trace every step!
• Signatures: Releases signed; catalog links evidence IDs—locked tight!
• Access: Unity Catalog roles—secure and smart!

SOP

1. Define Tasks: Write schema with constraints and vocabularies—start strong!
2. Ingest Sample: Validate, iterate—fine-tune it!
3. Scale Generation: Compute evidence, package—go for it!
4. Train & Publish: Evaluate at OP, publish cards—ship with pride!

FAQ

Can we merge multiple vocabularies?

Yes—namespace and map; document coverage and conflicts—keep it clear!

How do we keep splits stable?

Stratify by segments and vocab; lock seeds; record hashes—steady as she goes!

Appendix: Schemas & Prompts

schema.yaml, prompts.jsonl, eval_suites.json
  

Closing

With solid schemas and governed pipelines, LLM training becomes reproducible and safe. AethergenPlatform provides the scaffolding—so models ship with evidence, not surprises.

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