AI TRAINING
Anthropic API and Console Fundamentals
Build and deploy Claude-powered applications using Anthropic's API, Console, and core developer tooling.
What it covers
This hands-on training covers the full Anthropic developer stack: navigating the Console and Workbench, authenticating and calling the Messages API, selecting the right Claude model for a given workload, and implementing advanced features such as prompt caching, tool use, and streaming. Participants work through real coding exercises in Python and/or TypeScript. By the end they can scaffold a production-ready integration, optimise for latency and cost, and troubleshoot common API errors.
What you'll be able to do
- Authenticate to the Anthropic API and send correctly structured Messages API requests from a local or cloud environment
- Choose the appropriate Claude model variant for a given workload based on latency, cost, and capability requirements
- Implement prompt caching to reduce token costs by up to 90% on repeated context
- Define, call, and handle multi-turn tool-use loops to extend Claude with external functions and data sources
- Stream responses and handle partial events gracefully in a production Python or TypeScript service
- Diagnose and remediate common API errors including rate limits, malformed requests, and context-window overflows
Topics covered
- Anthropic Console and Workbench navigation and prompt prototyping
- Messages API structure: roles, content blocks, system prompts, and parameters
- Model selection: Claude 3 Haiku vs Sonnet vs Opus trade-offs for latency, cost, and capability
- Prompt caching: mechanics, TTL, cache-hit optimisation, and cost savings calculation
- Tool use (function calling): defining tools, handling tool_use blocks, and multi-turn tool loops
- Streaming responses with server-sent events in Python and TypeScript SDKs
- Error handling, retries, and rate-limit management in production
- Cost estimation, token counting, and API budget governance
Delivery
Delivered as a 2-day intensive bootcamp (in-person or live virtual). Day 1 focuses on Console exploration and core API mechanics; Day 2 covers advanced features and a capstone integration project. Approximately 60% hands-on coding, 40% instruction. Participants need a laptop with Python 3.10+ or Node 18+ and a valid Anthropic API key (trial credits sufficient). Materials include slide deck, Jupyter notebooks, and a private GitHub repo with starter code and solutions.
What makes it work
- Starting with a real internal use case as the capstone project so participants apply concepts immediately
- Establishing a shared API key management and cost-monitoring policy before the training ends
- Pairing engineers with a designated AI lead who reviews integrations one week post-training
- Using the Anthropic Workbench actively during development to iterate on prompts before hardcoding them
Common mistakes
- Passing the entire document corpus in every request instead of leveraging prompt caching, leading to unnecessary token spend
- Ignoring model-tier trade-offs and defaulting to the most capable (and expensive) model for every task
- Implementing tool use without validating tool_result blocks, causing silent failures in multi-turn loops
- Not implementing exponential back-off for rate-limit errors, resulting in brittle production integrations
When NOT to take this
A non-technical team (e.g., marketing or HR) that only needs to use Claude via a SaaS front-end and will never write API code — this training is overly technical and a prompt-engineering or tool-adoption workshop would serve them better.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.