📚 View Knowledge Base | 📊 Repository Index | 🔗 External References
The Copilot Architect Technical Knowledge Base is a comprehensive, production-focused guide for building enterprise-grade AI systems using Microsoft Copilot Studio, Azure AI Foundry, and Semantic Kernel.
Unlike typical documentation, this KB is the central source of truth that connects to 48 implementation repositories where every architectural pattern, use case, and code example is deployed and battle-tested in production.
Every repository implements a section of this Knowledge Base
Theory ↔ Practice | Knowledge ↔ Code | Architecture ↔ Deployment
The KB contains 7 major sections with corresponding implementation repositories:
Foundational patterns for enterprise Copilot solutions
Topics:
- Microsoft Copilot Stack (Azure OpenAI, Copilot Studio, Semantic Kernel)
- RAG Architectures (Vector DBs, Hybrid search, GraphRAG)
- Multi-Agent Systems (AutoGen, LangGraph, Agent orchestration)
- Production Deployment (API Gateway, Caching, Observability)
Implementation Repos: View All →
- semantic-kernel-production-patterns (Planned)
- rag-architecture-blueprints (Planned)
- three-layer-ai-framework ⭐
- azure-ai-foundry-showcase
- enterprise-agent-toolkit
Production deployments with metrics and lessons learned
Case Studies:
- Financial Services: Enterprise Knowledge Management (87% time reduction)
- Housing Association: Multi-Step Process Automation (45% faster)
- Local Government: Fine-tuned SLM for privacy
- Healthcare, Manufacturing, Retail (coming soon)
Implementation Repos: View All →
- enterprise-ai-analytics-platform ⭐
- agentic-data-platform
- strategic-forecasting-ai
- compliance-qa-production-system (Planned)
Battle-tested solutions to production AI problems
Challenge Categories:
- Evaluation & Quality: Hallucination detection, Prompt optimization, Consistency
- Performance & Scale: Latency (semantic caching), Cost optimization, Context limits
- Security & Governance: PII leakage, Prompt injection, RBAC
- Memory & State: Persistent memory patterns, Conversation management
Implementation Repos: View All →
- llm-evaluation-production-toolkit (Planned - P0)
- semantic-cache-production-patterns (Planned)
- ai-security-guardrails-framework (Planned)
- persistent-memory-patterns (Planned)
Decision frameworks with trade-off analysis
Decision Topics:
- RAG vs Fine-Tuning (decision tree, cost models, hybrid approach)
- Multi-Agent vs Simple Copilot (complexity analysis)
- Vector DB Selection (Cosmos DB, Pinecone, Qdrant, Azure AI Search)
- Deployment Patterns (Serverless vs K8s, PTU vs Pay-as-you-go)
Implementation Repos: View All →
- ai-architecture-decision-framework (Planned)
- rag-vs-finetuning-production-comparison (Planned)
Cutting-edge integrations and future trends
Emerging Topics:
- GraphRAG (Knowledge graph enhancement)
- Small Language Models (Privacy, cost, local deployment)
- Agentic RAG (Reasoning loops, query transformation)
- MCP Integration (Model Context Protocol)
Implementation Repos: View All →
- graphiti (Fork)
- mcp-use (Fork)
- graphrag-production-patterns (Planned)
- small-llm-local-deployment-guide (Planned)
Step-by-step production code examples
Guides Include:
- Build Production RAG System (Azure AI Search + OpenAI)
- Implement Semantic Caching (Redis with 60% cost savings)
- Multi-Agent Orchestration (AutoGen patterns)
- Prompt Flow Deployment (Azure AI Studio)
Implementation Repos: View All →
- kb-implementation-examples (Planned - P0)
- prompt-flow-production-templates (Planned)
- harringey-voicechatbot-AZURE (VoiceRAG fork)
Observable production systems
Metric Categories:
- Quality (Faithfulness, Relevancy, Hallucination rate)
- Performance (Latency, Throughput, Cache hit rate)
- Cost (Token usage, API costs, Infrastructure)
- Business (User satisfaction, Task completion, ROI)
Implementation Repos: View All →
- copilot-metrics-framework (Planned)
- llm-cost-calculator (Planned)
Repository: microsoft-ai-reality-check (Planned - P0)
Six LinkedIn articles with accompanying code implementations:
| Article | KB Sections | Working Code |
|---|---|---|
| 1. The Memory Problem | Challenges, Evolution | Redis memory patterns, Cosmos state management |
| 2. Platform Wrappers | Evolution | Abstraction layer examples, vendor comparison |
| 3. Customer Data Security | Challenges, Use Cases | PII detection code, compliance patterns |
| 4. Organizational Politics | Use Cases | Governance templates, stakeholder frameworks |
| 5. RAG Decision Framework | ADRs | Interactive decision tool, cost calculator |
| 6. Prompt Engineering Reality | Challenges | DSPy examples, Prompt Flow templates |
Each article repository includes:
- ✅ Full LinkedIn article (1,200-1,500 words)
- ✅ Technical deep dive (3x longer, 3,000-4,000 words)
- ✅ Runnable code examples
- ✅ Mermaid diagrams
- ✅ References back to KB sections
Repository: ba-genai-transformation-course
Current Site: http://65.109.4.220/ba-course/
5-week program for business analysts and project managers that uses this KB as the textbook:
- Week 1: Strategic Discovery → KB: Use Cases + ADRs
- Week 2: Workflow Foundations → KB: Architecture Patterns
- Week 3: Rapid Prototyping → KB: Implementation Guides
- Week 4: Delivery Pipelines → KB: Production Deployment
- Week 5: Agentic Operations → KB: Multi-Agent Systems
File: external-references.json
Curated collection of 60+ external repositories aligned with KB sections:
Semantic Kernel:
- SemanticKernel.Assistants (113 ⭐) - Multi-assistant patterns
- SemanticFlow (73 ⭐) - State management
- Legacy-Modernization-Agents (68 ⭐) - Microsoft official
Azure AI Foundry:
- Conversation-Knowledge-Mining (367 ⭐) - Microsoft official
- mcp-foundry (214 ⭐) - MCP integration
- gpt-rag-orchestrator (67 ⭐) - Agentic RAG
Multi-Agent:
- DeepMCPAgent (633 ⭐) - LangGraph + MCP
- langgraph-bigtool (452 ⭐) - Large tool sets
# Clone this repository
git clone https://github.com/maree217/copilot-architect-kb.git
cd copilot-architect-kb
# Open index.html in your browser
open index.html
# Or deploy to GitHub Pages (automatically served at maree217.github.io/copilot-architect-kb)# Install jq for JSON parsing (macOS)
brew install jq
# List all architecture pattern repos
cat mappings/repo-index.json | jq '.kb_sections["architecture-patterns"].repositories'
# List all planned P0 priority repos
cat mappings/repo-index.json | jq '[.. | select(.priority? == "P0")]'
# Find external repos with 100+ stars
cat mappings/external-references.json | jq '[.. | select(.stars? > 100)]'The KB includes client-side search (powered by Lunr.js):
- Open the KB in your browser
- Use Ctrl+F or Cmd+F to search
- Or use the search feature in the navigation menu
{
"total_repositories": 48,
"existing_repos": 16,
"planned_repos": 20,
"thought_leadership": 1,
"education": 1,
"forks_to_enhance": 5,
"external_references": 60,
"kb_sections": 7,
"code_examples": "50+",
"architecture_diagrams": "20+",
"production_case_studies": "4+",
"priority_distribution": {
"P0": 5,
"P1": 4,
"P2": 5,
"P3": 4,
"P4": 2
}
}Static docs → Disconnected code examples → No production context
Living KB ↔ Implementation Repos ↔ Production Metrics ↔ Thought Leadership ↔ Education
-
Bidirectional Linking
- KB → Repos (every pattern links to implementation)
- Repos → KB (every repo references KB sections)
-
Production-Proven
- Not toy examples—real client deployments
- Case studies with metrics (87% time reduction, 60% cost savings)
- Honest about what doesn't work
-
Comprehensive Coverage
- Architecture → Implementation → Metrics
- Theory → Practice → Results
- Beginner → Advanced → Expert
-
Thought Leadership Backed by Code
- LinkedIn articles aren't just writing—they have deployable repos
- Every claim is backed by working implementation
-
Educational Integration
- BA GenAI course uses KB as textbook
- Students learn from production patterns
- Certificate = KB proficiency
copilot-architect-kb/
├── index.html # Main KB (GitHub Pages)
├── README.md # This file
├── assets/
│ ├── diagrams/ # Mermaid diagrams (SVG export)
│ └── images/ # Screenshots, logos
├── sections/
│ ├── 01-architecture-patterns.md
│ ├── 02-use-cases.md
│ ├── 03-challenges.md
│ ├── 04-adrs.md
│ ├── 05-evolution.md
│ ├── 06-implementation.md
│ └── 07-metrics.md
├── mappings/
│ ├── repo-index.json # KB → Repos mapping
│ └── external-references.json # Curated external repos
├── updates/
│ ├── changelog.md # Version history
│ └── roadmap.md # Future plans
└── docs/
├── contributing.md # How to contribute
├── kb-architecture.md # KB architecture design
└── deployment.md # GitHub Pages setup
When the KB is updated, related repositories are automatically notified:
KB Update (e.g., new semantic caching pattern)
↓
Changelog entry created
↓
Related repos notified via GitHub Action
↓
Bot opens PR in implementation repos
↓
Review → Enhance → Merge
↓
Repo README updated with KB version reference
- Weekly: New challenges/solutions added
- Monthly: Use cases updated with client work
- Quarterly: New architecture patterns (emerging tech)
- Annually: Major version with new sections
We welcome contributions that enhance the KB or its implementation repositories!
- KB Content: Open an issue with tag
kb-enhancement - Implementation Repos: Find repos tagged
good first issue - External References: Submit PR to
external-references.json - Case Studies: Share production deployments (anonymized)
- All PRs must reference KB sections
- Code examples must be production-quality
- Include metrics where applicable
- Maintain bidirectional linking (KB ↔ Repo)
- ✅ 100% KB Coverage: Every KB section has ≥1 working repo
- ✅ 50+ Code Examples: All KB code deployable
- ✅ 20+ Diagrams: All diagrams link to implementations
- 🎯 500+ Stars: Aggregate across all repos
- 🎯 100+ Forks: Indicates reusability
- 🎯 10+ Contributors: External contributions
- 🎯 100+ Course Students: BA GenAI program completions
- 📝 10,000+ Article Views: LinkedIn engagement
- 📝 500+ Comments/Shares: Community discussion
- 📝 20+ Backlinks: External references to KB
- 💼 Track GitHub → Client Pipeline: KB as lead generation
- 💼 3+ New Case Studies: Production deployments
- 💼 Course Revenue Stream: Training monetization
This KB is maintained by Ram Maree, who holds:
- Microsoft Azure Solutions Architect Expert
- TOGAF 9.2 Certified
- PRINCE2 Practitioner
- Certified Business Analysis Professional (CBAP®)
With production experience deploying AI systems across:
- Financial Services (87% time reduction in compliance Q&A)
- Public Sector (45% faster repair processing)
- Healthcare, Manufacturing, Retail
- GitHub Profile: @maree217
- LinkedIn: Ram Maree
- Knowledge Base: copilot-architect-kb
- Course: BA GenAI Transformation
MIT License - See LICENSE file for details
This knowledge base builds on work from:
- Microsoft Azure AI Team (official samples)
- Semantic Kernel community
- LangChain/LangGraph contributors
- AutoGen framework developers
- 60+ curated external repositories
Special thanks to the open-source AI community for advancing the field.
- ✅ KB Hub created
- ✅ GitHub Pages deployment
- ✅ Repo index mapping
- ✅ External references curated
- kb-implementation-examples (P0)
- semantic-kernel-production-patterns (P0)
- rag-architecture-blueprints (P0)
- llm-evaluation-production-toolkit (P0)
- microsoft-ai-reality-check (P0)
- compliance-qa-production-system
- semantic-cache-production-patterns
- ai-security-guardrails-framework
- ai-architecture-decision-framework
- graphrag-production-patterns
- small-llm-local-deployment-guide
- mcp-copilot-integration-patterns
- multi-agent-orchestration-cookbook
- copilot-metrics-framework
- llm-cost-calculator
- ba-genai-transformation-course (GitHub migration)
- prompt-flow-production-templates
- Complete bidirectional linking
- Add GIF demos to READMEs
- Record video walkthroughs
- LinkedIn article publishing
- GitHub Project board
📚 Explore the Knowledge Base Now →
Last Updated: 2025-10-22 | Version 1.0.0 | Maintained by @maree217