Problem statements
Document current scoping bottlenecks and litigation risks so the AI program targets measurable gaps.
- Late discovery of missing baseline datasets
- Inconsistent Appendix G topic coverage
- Manual effort stitching project descriptions
root@ceqa:~$ cd /guides/ai-assisted-impact-scoping-playbook
AI-Assisted Impact Scoping Playbook
Equip CEQA teams with language models that can draft project descriptions, interrogate baseline datasets, and flag the impact topics most likely to trigger focused analysis—without sacrificing defensibility.
Level
Intermediate
Implementation window
4–6 week pilot
Core team
CEQA planner · Environmental analyst · NLP engineer
Key outcomes
Structured project narratives, validated baseline data, prioritized impact screening
Guide navigation
Stand up your impact scoping copilot
Move through discovery, model configuration, and operational rollout with reusable prompts, data validation routines, and stakeholder checklists built for CEQA timelines.
01 · Strategic framing
Anchor your scoping copilot to agency goals
Define how AI-assisted scoping advances agency priorities, from faster screening letters to defensible issue identification. Align stakeholders on scope, success metrics, and required safeguards before touching prompts.
Value hypotheses
Estimate time savings, quality gains, and risk reduction when LLMs draft narratives and scan inputs.
- Reduce first-draft scoping memos by 40%
- Flag 95% of missing required datasets before SME review
- Produce consistent issue matrices across projects
Guardrails
Establish model usage boundaries, human sign-offs, and documentation standards.
- Require planner validation on draft findings
- Log prompts, model versions, and outputs
- Segment sensitive location data at rest
02 · Data prerequisites
Audit baseline datasets before prompting
Impact scoping is only as strong as the inventories backing it. Stand up lightweight pipelines that catalog available data, score completeness, and route gaps for remediation.
Baseline data audit
Compile an extract describing datasets, formats, vintages, and custodians.
- Create metadata cards for land use, traffic, air quality, biology, noise
- Score freshness, spatial coverage, and regulatory adequacy
- Track gaps that block Appendix G topic evaluation
Data access pipelines
Integrate datasets into model-ready stores with clear access controls.
- Normalize formats using ETL/ELT workflows with data quality checks
- Provide planners a governed catalog with search and lineage
- Segment proprietary or personal data with policy-based access
Gap management playbook
Automate detection and routing of missing baseline inputs into remediation workflows.
Intake
LLM generates gap tickets mapped to responsible data owners.
Resolution
Assign retrieval actions, like commissioning surveys or sourcing GIS layers.
Verification
Require data stewardship sign-off before AI resumes scoping tasks.
03 · Prompt engineering
Design modular prompts that mirror CEQA scoping logic
Translate agency templates, Appendix G checklists, and jurisdictional thresholds into structured prompt components. Keep them version-controlled and reusable across project types.
Project narrative scaffold
Use chain-of-thought steps to gather inputs and produce consistent project description drafts.
- Solicit land use, construction phasing, utilities, approvals
- Generate tables for project components and phasing
- Insert citation placeholders linked to data sources
Baseline interrogation guide
Prompt the model to analyze data completeness and highlight missing supporting evidence.
- Ask the model to assess dataset timestamps versus policy thresholds
- Flag missing field surveys by season or species coverage
- Recommend targeted data collection tasks
Impact screening matrix
Generate annotated matrices that align to Appendix G categories and local thresholds.
- Score impact probability, severity, and data confidence
- Reference relevant regulatory triggers and guidance
- Highlight issues requiring SME interviews or fieldwork
04 · Automation modules
Operationalize the scoping copilot inside review workflows
Integrate LLM outputs with project management systems, GIS viewers, and document templates so planners see AI assistance within their existing tools.
Intake brief generator
Convert applicant submittals into standardized project briefs with embedded data checks.
- Parse PDFs, CAD exports, and application forms
- Flag missing attachments or inconsistent acreage totals
- Export briefs to project management tools
Impact prioritization engine
Pair LLM reasoning with rule-based thresholds to rank impact topics for SME focus.
- Crosswalk project features with Appendix G triggers
- Blend historical EIR review findings for similar projects
- Push high-risk topics to SME queues with justification
Scoping memo assembler
Auto-assemble scoping memos and IS/MND outlines with traceable citations and reviewer tasks.
- Insert baseline summaries and source references
- Create issue-specific task lists and due dates
- Generate export-ready Word or Google Docs packages
05 · Quality controls
Embed defensibility checks at every handoff
Pair AI efficiency with rigorous human review, documentation, and continuous monitoring to satisfy agency counsel and external reviewers.
Validation loops
Set up tiered QA to catch hallucinations, missing citations, and misclassified impacts.
- Automated linting for style, citations, and dataset versions
- Reviewer sign-offs for each impact topic within issue matrix
- Legal counsel review of thresholds and findings before release
Monitoring & feedback
Instrument usage analytics and escalate anomalies into remediation sprints.
- Track prompt effectiveness, data gap recurrence, and review cycle time
- Maintain a model change log with dataset refresh dates
- Run quarterly calibration sessions with planners and SMEs
06 · Implementation
Sequence pilots, training, and enterprise rollout
Launch small, measure impact, and scale responsibly. The roadmap below structures work into digestible waves with clear exit criteria.
Phase 01
Discovery sprint
Assess data availability, select pilot project, formalize success metrics.
- Stakeholder workshops
- Data gap inventory
- Legal review of guardrails
Phase 02
Pilot build
Configure prompts, integrate datasets, and run supervised scoping cycles.
- Model configuration
- Baseline QA dashboards
- Reviewer training sessions
Phase 03
Pilot evaluation
Measure accuracy, cycle time, and planner satisfaction before scaling.
- Calibration workshops
- Gap remediation backlog
- Executive go/no-go
Phase 04
Scale & sustain
Roll production workflows across programs with continuous improvement loops.
- Playbook publication
- Quarterly retraining cadence
- Enterprise-level support model
07 · Ops toolkit
Daily operating checklist
Use this quick reference to keep scoping copilots accurate, transparent, and aligned with CEQA best practices.
Before running prompts
- Confirm baseline datasets are refreshed and cataloged
- Load project-specific constraints (e.g., overlays, sensitive receptors)
- Review guardrails or legal updates affecting impact thresholds
After outputs generate
- Validate citations and dataset references
- Assign SMEs to high-priority impact findings
- Archive logs and reviewer annotations to the audit trail
08 · Further reading
Templates, tools, and references
Extend the playbook with these starting points and benchmarks.
Template bundles
- Appendix G impact screening matrix (CSV + prompt)
- Project description chain-of-thought prompt library
- Baseline data audit workbook (Google Sheets)
Reference standards
- Governor’s Office of Planning and Research technical advisories
- Regional air quality management plan thresholds
- USFWS and CDFW species survey protocols