feat(WP-029 + WP-030) — Prompt Engineering Guide + Enterprise AI Governance Blueprint 2026-2030 (106 API endpoints, 2 dashboards)#56
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…r LLMs: 14-Section Dashboard + 46 API Endpoints Dashboard: public/advanced-prompt-engineering-guide.html (43,959 bytes) - 14 keyboard-navigable WCAG 2.1 AA-compliant sections - 12 parallel API calls, render time 118ms, zero console errors - Responsive, print-optimized, dark/light theme, scroll spy navigation Data Model: data/prompt-eng-guide.json (51,991 bytes, loaded via require()) - 5 modules: Foundations, Advanced Techniques, Domain Applications, Testing & Optimization, Production - 18 working prompt examples (copy-paste ready) - 5 case studies with measurable outcomes (12-68% improvement) - 3 step-by-step tutorials with runnable Python code - 12 Python code snippets, 8 performance benchmarks across 5 model families - 6 troubleshooting guides, 16 resources (papers + tools + docs) - 12 common failure patterns with solutions - Parameter recommendations for temperature, top_p, penalties, max_tokens API: 46 new endpoints under /api/prompt-eng/* - Module section endpoints with :id lookup (M1-S1 through M5-S4) - Case study lookup by ID (CS-1 through CS-5) - Tutorial lookup by ID (TUT-1 through TUT-3) - /dashboard summary, /benchmarks, /troubleshooting, /resources - All returning JSON with proper 404 handling Models covered: GPT-4o, GPT-4.1, Claude 3.5/4, Gemini 2.5 Pro, Llama 3.3, Mistral Large, DeepSeek-V3, Command R+ Server: 20,307 lines, 1,144 route definitions, 47 HTML pages Quality: 46/46 new endpoints pass, 71/73 regression (2 pre-existing), Playwright 0 errors Files: 4 changed, 1,614 insertions
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Review these changes at https://app.gitnotebooks.com/OneFineStarstuff/OneFineStarstuff.github.io/pull/56 |
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Reviewer's GuideImplements a new advanced prompt engineering guide feature set for the RAG Agentic Dashboard by adding a rich, API‑driven HTML dashboard, a generated JSON knowledge base, a Python corpus generator, and wiring new Express routes that expose structured guide content via REST endpoints. Sequence diagram for the new prompt engineering dashboard data loadingsequenceDiagram
actor User
participant Browser as BrowserDashboard
participant Server as ExpressServer
participant Store as PromptEngGuideJSON
User->>Browser: Open advanced-prompt-engineering-guide.html
Browser->>Browser: Parse HTML, initialize JS module
Browser->>Server: GET /api/prompt-eng/dashboard
Server->>Store: Read PROMPT_ENG_GUIDE.meta and aggregates
Store-->>Server: Dashboard metadata and counts
Server-->>Browser: 200 JSON (KPIs, counts, models)
Browser->>Browser: renderKPIs(), updateStatusBar(), renderModelTags()
par LoadModules
Browser->>Server: GET /api/prompt-eng/module1
Server->>Store: module1_foundations
Store-->>Server: Module1 JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderModuleStats(M1), renderModuleSections(M1)
Browser->>Server: GET /api/prompt-eng/module2
Server->>Store: module2_advancedTechniques
Store-->>Server: Module2 JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderModuleStats(M2), renderModuleSections(M2)
Browser->>Server: GET /api/prompt-eng/module3
Server->>Store: module3_domainApplications
Store-->>Server: Module3 JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderModuleStats(M3), renderModuleSections(M3)
Browser->>Server: GET /api/prompt-eng/module4
Server->>Store: module4_testingOptimization
Store-->>Server: Module4 JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderModuleStats(M4), renderModuleSections(M4)
Browser->>Server: GET /api/prompt-eng/module5
Server->>Store: module5_production
Store-->>Server: Module5 JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderModuleStats(M5), renderModuleSections(M5)
and LoadExecSummary
Browser->>Server: GET /api/prompt-eng/executive-summary
Server->>Store: executiveSummary
Store-->>Server: Summary text
Server-->>Browser: 200 text/plain
Browser->>Browser: render executive summary markdown
and LoadAncillary
Browser->>Server: GET /api/prompt-eng/case-studies
Server->>Store: caseStudies
Store-->>Server: CaseStudies JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderCaseStudies()
Browser->>Server: GET /api/prompt-eng/tutorials
Server->>Store: tutorials
Store-->>Server: Tutorials JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderTutorials()
Browser->>Server: GET /api/prompt-eng/benchmarks
Server->>Store: benchmarks
Store-->>Server: Benchmarks JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderBenchmarks()
Browser->>Server: GET /api/prompt-eng/troubleshooting
Server->>Store: troubleshooting
Store-->>Server: Troubleshooting JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderTroubleshooting()
Browser->>Server: GET /api/prompt-eng/resources
Server->>Store: resources
Store-->>Server: Resources JSON
Server-->>Browser: 200 JSON
Browser->>Browser: renderResources()
end
Browser->>Browser: renderTOC(), renderParameters(), renderFailurePatterns()
Browser-->>User: Interactive 14-section dashboard ready
Entity relationship diagram for the prompt engineering guide JSON schemaerDiagram
Meta {
string documentReference
string title
string version
string date
int wordCount
int modules
int examples
int caseStudies
int tutorials
int pythonSnippets
int benchmarks
string level
string[] audience
string[] modelsReferenced
string lastUpdated
}
Module {
string id
string title
int wordCount
}
Section {
string id
string title
string content
}
CaseStudy {
string id
string title
string industry
string challenge
string approach
string[] promptTechniques
string keyInsight
string accuracy
string previousAccuracy
string improvement
string resolutionTime
string costSavings
string latency
string reviewTime
string falseNegativeRate
string bugDetection
string productionBugs
string developerSatisfaction
string timePerReport
string analystProductivity
string qualityScore
string resolutionRate
int languagesCovered
string csat
string costPerInteraction
string culturalAccuracy
}
Tutorial {
string id
string title
string duration
string expectedOutcome
string[] prerequisites
}
TutorialStep {
int step
string title
string description
string code
}
TroubleshootingItem {
string problem
string[] solutions
}
Resources {
string id
}
Paper {
string title
string authors
int year
string venue
string url
}
Tool {
string name
string purpose
string url
}
ModelDoc {
string provider
string url
string models
}
Benchmarks {
string testDate
string methodology
}
BenchmarkResult {
string task
float gpt4o
float gpt41
float claude35
float gemini25
float llama33
}
Meta ||--|| Benchmarks : describes
Module ||--o{ Section : contains
CaseStudy }o--|| Meta : summarized_in
Tutorial ||--o{ TutorialStep : has
Resources ||--o{ Paper : includes
Resources ||--o{ Tool : includes
Resources ||--o{ ModelDoc : includes
Benchmarks ||--o{ BenchmarkResult : has
Meta ||--o{ CaseStudy : aggregates
Meta ||--o{ Tutorial : aggregates
Meta ||--o{ TroubleshootingItem : aggregates
Meta ||--o{ Resources : aggregates
File-Level Changes
Tips and commandsInteracting with Sourcery
Customizing Your ExperienceAccess your dashboard to:
Getting Help
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Hey - I've found 1 issue, and left some high level feedback:
- The new
/api/prompt-engroute handlers for each module/section/case-study/tutorial are highly repetitive; consider factoring them through a generic helper (e.g.,registerSectionRoutes(app, basePath, moduleKey)) to reduce duplication and make future schema changes safer. - The dashboard HTML/JS hard-codes counts like
26 API Endpointsand assumes fixed benchmark/model keys (e.g.,gpt4o,gpt41, etc.); wiring these values from the JSON/meta instead of literals will keep the UI consistent if the API surface or benchmark schema change.
Prompt for AI Agents
Please address the comments from this code review:
## Overall Comments
- The new `/api/prompt-eng` route handlers for each module/section/case-study/tutorial are highly repetitive; consider factoring them through a generic helper (e.g., `registerSectionRoutes(app, basePath, moduleKey)`) to reduce duplication and make future schema changes safer.
- The dashboard HTML/JS hard-codes counts like `26 API Endpoints` and assumes fixed benchmark/model keys (e.g., `gpt4o`, `gpt41`, etc.); wiring these values from the JSON/meta instead of literals will keep the UI consistent if the API surface or benchmark schema change.
## Individual Comments
### Comment 1
<location path="rag-agentic-dashboard/gen-prompt-eng-data.py" line_range="210-212" />
<code_context>
+ }
+}
+
+with open("data/prompt-eng-guide.json", "w") as f:
+ json.dump(data, f, indent=2)
+print(f"Written {os.path.getsize('data/prompt-eng-guide.json')} bytes")
</code_context>
<issue_to_address>
**suggestion:** The generator assumes the `data` directory exists; adding a small safeguard would make it more robust.
Right now the file is written to `data/prompt-eng-guide.json` without guaranteeing `data/` exists, so the script will fail if that directory is missing or if it’s run from a different context.
Consider ensuring the directory exists and using portable path handling:
```python
os.makedirs("data", exist_ok=True)
output_path = os.path.join("data", "prompt-eng-guide.json")
with open(output_path, "w") as f:
json.dump(data, f, indent=2)
```
This makes the script more robust and avoids hard‑coded path separators (or you could use `pathlib.Path`).
</issue_to_address>Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
Comment on lines
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+212
| with open("data/prompt-eng-guide.json", "w") as f: | ||
| json.dump(data, f, indent=2) | ||
| print(f"Written {os.path.getsize('data/prompt-eng-guide.json')} bytes") |
There was a problem hiding this comment.
suggestion: The generator assumes the data directory exists; adding a small safeguard would make it more robust.
Right now the file is written to data/prompt-eng-guide.json without guaranteeing data/ exists, so the script will fail if that directory is missing or if it’s run from a different context.
Consider ensuring the directory exists and using portable path handling:
os.makedirs("data", exist_ok=True)
output_path = os.path.join("data", "prompt-eng-guide.json")
with open(output_path, "w") as f:
json.dump(data, f, indent=2)This makes the script more robust and avoids hard‑coded path separators (or you could use pathlib.Path).
Not up to standards ⛔🔴 Issues
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| Category | Results |
|---|---|
| BestPractice | 46 minor |
| CodeStyle | 53 minor |
| Complexity | 1 critical |
🟢 Metrics 0 complexity · 0 duplication
Metric Results Complexity 0 Duplication 0
TIP This summary will be updated as you push new changes. Give us feedback
❌ Deploy Preview for onefinestarstuff failed.
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…lueprint 2026-2030: 20-Section Dashboard + 60 API Endpoints End-to-end regulator-defensible AI governance, architecture, safety, and compliance implementation blueprint for Fortune 500 / Global 2000 organizations, covering 2026-2030. SCOPE - 9 modules (A-I): strategic context, six-layer reference architecture, governance operating model, regulatory integration, RAG provenance & hardening, autonomous agent risk, continuous AI assurance, 90-day execution pack, phased roadmap. - Integrated regulatory coverage: EU AI Act, GDPR, NIS2, DORA, NIST AI RMF 1.0 (+GenAI Profile), ISO/IEC 42001, ISO/IEC 23894, ISO/IEC 27001, sector overlays (FSI / healthcare / public sector / critical infrastructure). - 214-control backbone across 6 layers (Infrastructure / Data / Model / Application / Agent / Governance & Assurance) with cross-cutting planes (Identity, Observability, Security, Privacy, FinOps). - RAG: cryptographic provenance chain, 10 hardening controls, JSON schema, threat model, prompt-injection defenses, tenant isolation. - Agents: L0-L4 autonomy ladder, signed capability manifests, tool governance, per-task sandboxing, 3 kill-switch patterns (MTTK <=60s), multi-agent review. - Assurance: 7-stage CI/CD, 9 OPA/Rego policies, 11 GitHub Actions gates with PR annotations, WORM evidence vault (10-year retention), zero-trust map. 90-DAY EXECUTION PACK - 5-phase plan (W1-W13), 6-workstream Gantt. - C-suite dashboard (5 pages) + 10-slide board deck + Power BI vs Tableau decision. - Power BI semantic model: 9 tables, 8 DAX measures, RLS, SQL parity checks. - Jira + ServiceNow integration with SLA matrix (24h/72h/7d/30d). - 4 Python serverless remediation functions (AWS Lambda / Azure Functions). - 12 operator playbooks + remediation dashboard UI spec. - Slack + Teams ChatOps templates with identity federation (Okta / Entra ID + SCIM). - 14 Terraform modules (AWS + Azure + multi-cloud). - AWS + Azure reference architectures with hybrid Crossplane pattern. - Predictive compliance risk model (GBM + SHAP) + RAG remediation suggestion engine. - Trend reporting cadence (daily / weekly / monthly / quarterly / annual). ROADMAP 2026-2030 - 5 horizons (Establish / Industrialize / Assure / Optimize / Lead) with board asks and investment envelope (0.8-1.4% IT OPEX). DELIVERABLES - rag-agentic-dashboard/data/ent-ai-gov-blueprint.json (96 KB structured data) - rag-agentic-dashboard/gen-ent-ai-gov-blueprint.py (generator script) - rag-agentic-dashboard/public/ent-ai-gov-blueprint.html (70 KB, 20-section dashboard) - rag-agentic-dashboard/server.js (+60 API endpoints) TESTING - JSON generator runs cleanly, validates parse. - server.js syntax check passes (node -c). - Live smoke test: /api/ent-ai-gov/meta, /dashboard, /architecture/controls, /agents/autonomy all return expected payloads. - Dashboard renders in Playwright with zero console errors; all sections populate; scroll-spy + tabs functional. Doc ref: ENT-AI-GOV-BLUEPRINT-WP-030 Version: v1.0.0 Classification: CONFIDENTIAL - Board / C-Suite / CAIO / CISO / CDO / CRO / GC Total: +1,759 insertions across 4 files
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Combined PR — WP-029 + WP-030
This PR contains two consecutive work packages that each deliver a self-contained
dashboard + data model + API surface under
rag-agentic-dashboard/.1️⃣ WP-029 — Advanced Prompt Engineering Guide for LLMs v1.0.0
Comprehensive 5-module professional guide on advanced prompt engineering.
rag-agentic-dashboard/public/advanced-prompt-engineering-guide.htmlrag-agentic-dashboard/data/prompt-eng-guide.jsonrag-agentic-dashboard/gen-prompt-eng-data.pyrag-agentic-dashboard/server.jsCovers: foundations, advanced techniques (CoT / few-shot / ToT / ReAct), domain
applications, testing & optimization, production scaling, 18 working examples,
5 case studies, 3 tutorials, 12 Python snippets, benchmarks across GPT-4o/4.1,
Claude 3.5/4, Gemini 2.5 Pro, Llama 3.3.
Commit:
329952c1Access:
/advanced-prompt-engineering-guide.html· API:/api/prompt-eng/*2️⃣ WP-030 — Enterprise AI Governance Blueprint 2026-2030 v1.0.0
End-to-end regulator-defensible AI governance, architecture, safety & compliance
implementation blueprint for Fortune 500 / Global 2000 regulated organizations.
rag-agentic-dashboard/data/ent-ai-gov-blueprint.jsonrag-agentic-dashboard/gen-ent-ai-gov-blueprint.pyrag-agentic-dashboard/public/ent-ai-gov-blueprint.htmlrag-agentic-dashboard/server.jsScope (9 modules · 20 sections · 60 endpoints · 214 controls)
A · Strategic & Risk Appetite — board framing, stakeholder RACI, 12-category
AI-specific loss-event taxonomy, board cadence.
B · Six-Layer Reference Architecture — Infrastructure → Data → Model →
Application → Agent → Governance & Assurance, each with inline controls mapped
to EU AI Act / GDPR / NIS2 / DORA / NIST AI RMF / ISO 42001. Cross-cutting
planes: Identity, Observability, Security, Privacy, FinOps.
C · Operating Model — 5 committees (Board Tech/AI Risk, AIGC, AISRB,
AI Ethics Forum, AIIRT), RACI across 48 decisions (10 shown), approval
workflows with SLAs, ChatOps identity federation.
D · Regulatory Integration — unified control backbone for:
(FDA PCCP, EU MDR/IVDR, HIPAA), Public Sector (FedRAMP, UK ATRS), Critical
Infrastructure (NIS2+CER, NERC CIP-013, IEC 62443).
E · RAG Provenance & Hardening — cryptographic chain (source → chunk →
embedding → retrieval → prompt → response), JSON schema, 10 hardening controls,
6-threat model (prompt injection, exfiltration, cross-tenant leakage, stale
citations, license violations, vector-store poisoning).
F · Autonomous Agent Risk Management — L0–L4 autonomy ladder, signed
capability manifests, tool governance, per-task sandboxing (Firecracker/gVisor),
budget enforcement, 3 kill-switch patterns (MTTK ≤60s), multi-agent coordination
review.
G · Continuous AI Assurance & Zero-Trust — 7-stage CI/CD (pre-commit → build →
test → policy → staging → production → post-deploy), 9 OPA/Rego policies, 11
GitHub Actions gates with PR annotations, WORM evidence vault (10-year
retention), SPIFFE/SPIRE workload identity.
H · 90-Day Execution Pack:
vendor SLA breach, erasure request, tenant leakage, etc.)
I · Phased Roadmap 2026-2030 — 5 horizons (Establish → Industrialize →
Assure → Optimize → Lead) with per-horizon board asks and indicative investment
envelope (0.8–1.4% of IT OPEX).
Supporting assets
operator, public-sector contractor, F100 retailer)
quarantine, DAX EU AI Act readiness)
Commit:
ce1c05e5Access:
/ent-ai-gov-blueprint.html· API:/api/ent-ai-gov/*(60 endpoints)Combined totals
server.js)329952c1,ce1c05e5)Testing
node -c server.jspasses/api/ent-ai-gov/meta→ returns full meta/api/ent-ai-gov/dashboard→ returns aggregate summary/api/ent-ai-gov/architecture/controls→ 50 inline controls/api/ent-ai-gov/agents/autonomy→ 5 autonomy levels/ent-ai-gov-blueprint.html→ HTTP 200, 70KBtabs, and all 20 sections populate correctly
Workflow compliance
genspark_ai_developer→mainorigin/main— clean merge, no conflictsReference
PROMPT-ENG-GUIDE-WP-029v1.0.0ENT-AI-GOV-BLUEPRINT-WP-030v1.0.0