root@ceqa:~$ cd /guides/model-training-tips

Model Training Tips

Build domain-tuned AI models—LLMs, classifiers, retrieval systems—that respect CEQA context, cite defensibly, and empower environmental analysts without risking accuracy.

Level

Intermediate → Advanced

Implementation window

Ongoing program (pilot in 8–12 weeks)

Core team

ML lead · CEQA SMEs · Data engineer · QA analyst

Key outcomes

High-accuracy models, traceable outputs, continuous improvement

Guide navigation

Train models that planners trust

Use these modules to scope training objectives, curate data, choose model strategies, enforce QA, and sustain model health for CEQA-focused AI.

1. Training goals 2. Priority use cases 3. Data strategy 4. Model selection & tuning 5. Training pipeline 6. Evaluation & QA 7. Deployment & MLOps 8. Governance & ethics 9. Operating checklist 10. Resources
01 · Alignment

Define why you are training models

Anchor your model roadmap in CEQA review realities. Pick objectives that reduce reviewer toil, improve consistency, and withstand legal scrutiny.

Productivity & focus

Use AI to pre-screen documents, flag risks, and draft sections so planners focus on judgment-heavy work.

Consistency & defensibility

Deliver standardized outputs with citations and checklists that align with agency templates and case law.

Insight acceleration

Turn raw data into alerts, dashboards, and narratives that keep CEQA projects on schedule.

02 · Select high-value models

Prioritize models that deliver measurable value

Start with models where you have quality data, clear success metrics, and quick reviewer feedback loops.

Appendix G impact classifier

Label sentences or sections by resource area and significance level to triage review effort.

Mitigation extraction model

Pull mitigation measures, responsible parties, and monitoring details into structured tables.

Comment response assistant

Summarize comment themes and draft response templates with citations to relevant sections.

Spatial risk scoring

Score project footprints against regulatory thresholds (noise, AQ, bio) using integrated GIS data.

Defensibility advisor

Flag statements lacking citations or conflicting with precedent, referencing case law knowledge bases.

Workflow orchestrator

Predict review bottlenecks and recommend task assignments based on history and project complexity.

03 · Data strategy

Curate training data the right way

CEQA documents are long, technical, and sensitive. Build data pipelines that maintain context, respect privacy, and capture reviewer expertise.

Data sourcing

  • Collect approved CEQA/NEPA documents, technical studies, comment logs, mitigation registers
  • Capture reviewer edits and track-change histories to learn preferred language
  • Include geographic, regulatory, and project metadata for conditioning

Preprocessing

  • Segment documents into hierarchical chunks with IDs
  • Normalize terminology (resource area names, mitigation categories)
  • Apply OCR, de-duplication, and redact sensitive data when needed

Annotation workflow

  • Design labeling guides aligned with Appendix G, agency heuristics, and legal thresholds
  • Use active learning to prioritize ambiguous samples for SMEs
  • Track inter-annotator agreement and adjudicate conflicts

Dataset governance

  • Maintain dataset versions with lineage and release notes
  • Document usage rights, retention policies, and confidentiality rules
  • Store embeddings and features securely for reuse
04 · Model strategy

Pick the right model approach for each task

Balance accuracy, cost, latency, and deployability. Mix foundation models, fine-tuning, retrieval, and classical ML depending on requirements.

Large language models

  • Use retrieval-augmented generation (RAG) for citation-rich drafting
  • Fine-tune on in-domain content or apply LoRA adapters
  • Implement guardrails: prompt templates, citation enforcement, safety filters

Classical NLP & ML

  • Train gradient boosting or SVM models for structured predictions (significance scoring, risk flags)
  • Use spaCy or transformers for named entity recognition (mitigation, agency, location)
  • Deploy rule-based overlays for deterministic requirements

Multimodal approaches

  • Combine text + GIS embeddings for spatial risk scoring
  • Integrate tables, figures, and maps using layout-aware models
  • Leverage time-series models for monitoring data forecasting
05 · Training pipeline

Operationalize the end-to-end training workflow

Structure your pipeline so models can be retrained, audited, and improved without guesswork.

  1. Define experiment charter. Document objectives, baselines, metrics, risks, and reviewers. Align with legal and IT stakeholders.
  2. Assemble dataset. Pull curated data slices, apply preprocessing, and store splits (train/val/test) with reproducible seeds.
  3. Train & track. Use MLflow/Weights & Biases for experiment tracking, hyperparameter sweeps, and artifact storage.
  4. Validate with SMEs. Present outputs to CEQA reviewers for qualitative review, gather annotations, and iterate.
  5. Document model card. Record intended use, limitations, datasets, metrics, and human oversight requirements.
  6. Promote candidate. Run acceptance tests, compare against baselines, and seek governance board approval before deployment.
06 · Evaluation & QA

Measure accuracy, alignment, and defensibility

Establish a multi-layered QA stack: automated metrics, human review, and litigation-readiness artifacts.

Quantitative metrics

  • Accuracy, precision/recall, F1 for classification tasks
  • BLEU, ROUGE, or domain-specific metrics for summarization
  • Calibration metrics and confidence intervals

Human evaluation

  • Reviewer scorecards (accuracy, completeness, clarity)
  • Red-team exercises focusing on hallucinations and bias
  • Time-to-approval reduction for draft outputs

Defensibility artifacts

  • Prompt libraries with version history
  • Model card + data sheet stored with project records
  • Audit logs for training runs and reviewer overrides
07 · Deployment & MLOps

Operationalize models in production workflows

Ensure models integrate smoothly with document authoring tools, dashboards, and APIs while remaining maintainable.

Serving patterns

  • Batch scoring for scheduled reports and QA reviews
  • Real-time APIs feeding review dashboards or copilots
  • Edge deployments for sensitive on-prem environments

Monitoring & drift

  • Track data drift, prediction drift, and business KPIs
  • Alert reviewers when confidence drops or anomalies appear
  • Schedule retraining or prompt updates based on feedback

Change management

  • Document release notes with expected behavior changes
  • Run training sessions and choose adoption champions
  • Provide rollback plans and manual fallback procedures

Integration enablers

  • APIs/SDKs for project management, document systems, GIS
  • Feature stores and vector databases shared across teams
  • Automation hooks into CEQA review dashboards
08 · Governance & ethics

Keep models ethical, transparent, and accountable

Formalize governance so AI assists planners without undermining public trust or compliance obligations.

Oversight structure

  • Create an AI review board with CEQA leads, legal, IT security
  • Schedule quarterly model audits and risk assessments
  • Require sign-off before models influence public releases

Ethical guardrails

  • Document limitations and ensure human override remains easy
  • Screen for bias in metrics, language, and spatial recommendations
  • Communicate AI involvement to stakeholders in plain language
09 · Operating checklist

Checklist for every training cycle

Keep your program disciplined with this recurring checklist. Adapt per model type and jurisdiction.

Before training

  • Business objective and success metrics approved
  • Dataset inventory and consent/legal review complete
  • Annotation plan and SME bandwidth confirmed
  • Baseline model and benchmarks documented

During training

  • Experiments logged with reproducible configs
  • Data and model metrics tracked in dashboards
  • SME review sessions scheduled and recorded
  • Security/privacy checks on artifacts performed

After deployment

  • Monitoring alerts configured and tested
  • Model card, prompt library, and SOPs published
  • Retraining triggers and cadence defined
  • Lessons learned fed into backlog
10 · Resources

Toolkits, templates, and references

Use these artifacts to bootstrap your CEQA-focused ML program. Replace with agency-specific materials as you scale.

  • Model charter template: Capture problem statement, metrics, risks, and oversight for each training effort.
  • Annotation style guide: Instructions, examples, and decision trees for consistent labeling.
  • Experiment tracking notebook: Prebuilt MLflow/W&B integration with CEQA-specific metadata fields.
  • Human evaluation rubric: Scorecard format for reviewers covering accuracy, completeness, and defensibility.
  • Retraining playbook: Trigger matrix, retraining cadence, and communication plan.

Need help launching or auditing CEQA-aware models? CEQA.ai partners with teams on data curation, fine-tuning, and trustworthy deployment strategies.