Course Overview
Building production applications with the Anthropic Claude API requires more than basic text completions. This course is built for software engineers, tech leads, and AI architects who want to develop dependable, stateful, and cost-efficient LLM integrations.
By completing this curriculum, you will master the Messages API paradigm, structure deterministic tool schemas, integrate external databases via Model Context Protocol (MCP), and optimize latency through prompt caching.
Structured Curriculum & Modules
Understanding the Anthropic Messages API, stateless conversations, system prompts, role orchestration (user/assistant), and token context management.
- Setup Anthropic SDK in TypeScript and Python
- Configuring system instructions and temperature controls
- Handling multi-turn conversations and sliding windows
Specifying JSON schema output boundaries, few-shot exemplars, thinking/reasoning budgets, and deterministic schema enforcement.
- XML tagging conventions for clean prompt separation
- Techniques for reliable JSON serialization without truncation
- Implementing fallback retry strategies on malformed outputs
Equipping Claude with real-world tool execution capabilities. Defining JSON schemas, resolving tool call requests, and integrating MCP servers.
- Tool definitions, parameter constraints, and descriptions
- The agentic execution loop: prompt → tool_use → execution → tool_result
- Connecting to Postgres and GitHub via official MCP clients
Optimization techniques for enterprise production. Prompt caching to slash costs by 90%, rate limit management, and automated evaluation pipelines.
- Implementing prompt caching breakpoints effectively
- Building custom CI/CD eval runners with regression datasets
- Production telemetry, cost accounting, and latency monitoring
Capstone Project
In the final project, you will build an autonomous Customer Intelligence & Ticket Resolution Agent that:
- Reads inbound support briefs and categorizes urgency via Haiku.
- Inspects account records in a mock database using tool execution.
- Generates a draft response alongside an internal JSON audit record via Sonnet.
