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Build Exercises
Each lesson includes a hands-on build exercise. These are actual projects you can build to cement your understanding of each concept.
Domain 1: Solution Design & Architecture
6 exercises
- 1.1 Translate business problems into Claude-based AI solutions
- 1.2 Design end-to-end architectures (input → processing → output → feedback loops)
- 1.3 Select appropriate architectural patterns (workflow, agentic, augmented LLM)
- 1.4 Design multi-agent systems and orchestration strategies
- 1.5 Apply decomposition techniques for complex problem solving
- 1.6 Align solutions to business value pillars (efficiency, transformation, productivity, cost, performance SLAs)
Domain 2: Claude Models, Prompting & Context Engineering
5 exercises
- 2.1 Select appropriate Claude models based on trade-offs
- 2.2 Design system prompts, templates, and guardrails
- 2.3 Apply prompt engineering techniques (zero-shot, few-shot, chain-of-thought)
- 2.4 Optimize context windows and manage token usage
- 2.5 Implement prompt reuse strategies (caching, modular prompts, Skills)
Domain 3: Integration
8 exercises
- 3.1 Evaluate tool/agent configuration for capability bloat
- 3.2 Analyze authentication and authorization requirements to identify security gaps
- 3.3 Evaluate accuracy-latency trade-offs and justify configuration decisions
- 3.4 Analyze observability challenges and select monitoring strategies at scale
- 3.5 Design a RAG pipeline with appropriate chunking and indexing strategies
- 3.6 Apply retrieval strategies matched to data shape and query pattern
- 3.7 Evaluate connection protocols and select the appropriate integration mechanism (MCP, API/CLI, agent-to-agent)
- 3.8 Evaluate progressive discovery vs. monolithic context strategy
Domain 4: Evaluation, Testing & Optimization
6 exercises
- 4.1 Define evaluation metrics (accuracy, latency, cost, safety, security)
- 4.2 Design evaluation datasets and test frameworks using mixed methodologies
- 4.3 Conduct A/B testing and iterative improvements
- 4.4 Diagnose system issues (prompt failure, hallucinations, model mismatch)
- 4.5 Optimize token usage, latency, and cost-performance trade-offs
- 4.6 Monitor system performance using logging and observability tools
Domain 5: Governance, Safety & Risk Management
5 exercises
- 5.1 Implement guardrails and safety controls
- 5.2 Identify risks, limitations, and failure modes of LLM systems
- 5.3 Apply human-in-the-loop validation strategies
- 5.4 Ensure compliance with regulations (e.g., GDPR, HIPAA, FedRAMP)
- 5.5 Address ethical AI considerations (bias, fairness, transparency)
Domain 6: Stakeholder Communication & Lifecycle Management
5 exercises
- 6.1 Conduct structured discovery and requirement gathering
- 6.2 Communicate architectural decisions and trade-offs
- 6.3 Manage stakeholder feedback loops and expectation alignment (including SLAs)
- 6.4 Document architectures and provide implementation guidance
- 6.5 Support lifecycle phases (discovery, design, handoff, monitoring, iteration)
Domain 7: Developer Productivity & Operational Enablement
3 exercises
- 7.1 Configure Claude tools and environments for teams (e.g., Claude Code)
- 7.2 Improve developer workflows using AI-assisted tooling
- 7.3 Support debugging and operational issue resolution