Anthropic courses

Teaches developers how to authenticate with Anthropic's API using SDK setup, API key management, and environment configuration. The course module covers authentication flows, model selection (Claude 3 variants), and parameter tuning through hands-on examples using Python SDK, progressing from basic setup to advanced configuration patterns like streaming and multimodal inputs.

Delivers structured lessons on core prompting techniques including role prompting, instruction-data separation, output formatting, chain-of-thought reasoning, and few-shot learning through Jupyter notebook-based interactive tutorials. Each technique is taught with concrete examples, anti-patterns, and hands-on exercises that learners execute against live Claude API calls, building intuition for prompt design patterns.

Teaches techniques for reducing hallucinations and improving output reliability through prompt design strategies such as explicit instruction to acknowledge uncertainty, constraining output formats, providing reference materials, and using verification steps. The course covers both preventive techniques (prompt design) and detective techniques (output validation) for building more reliable LLM applications.

Teaches how to improve Claude's performance on specific tasks by providing examples of desired input-output pairs within the prompt (few-shot learning). The course covers example selection strategies, formatting conventions for examples, and techniques for determining how many examples are needed for different task types.

Teaches developers how to leverage Claude's vision capabilities by processing images alongside text in prompts. The course module covers image input formats, vision-specific parameters, and practical patterns for tasks like image analysis, OCR, and visual reasoning, with examples demonstrating how to structure multimodal requests through the Python SDK.

Teaches systematic methods for measuring and improving prompt quality through human-graded evaluations, code-graded evaluations, model-graded evaluations, and custom evaluation systems. The course covers evaluation metrics, test harness design, and integration with the Promptfoo framework for automated evaluation pipelines, enabling developers to establish quality gates for prompt changes.

Demonstrates application of prompt engineering techniques to complex, real-world scenarios through detailed case studies that show the full workflow from problem definition through prompt iteration and evaluation. Each case study walks through specific application domains (e.g., customer support, content generation, data extraction) with concrete prompts, common pitfalls, and optimization strategies derived from production experience.

Teaches developers how to implement Claude's tool-using capabilities by defining tool schemas, handling tool calls in application logic, and building workflows where Claude decides when and how to use available tools. The course covers tool schema definition, error handling for tool execution, and patterns for building multi-step agentic workflows where Claude orchestrates tool use across multiple steps.

Organizes educational content as a coherent learning path where each course builds on prior knowledge, with explicit prerequisites and recommended progression from API fundamentals through prompt engineering to evaluation and real-world applications. The curriculum design ensures learners develop foundational understanding before tackling advanced topics, reducing cognitive load and enabling effective knowledge transfer.

Provides guidance on selecting appropriate Claude models for different use cases with emphasis on cost optimization, using Claude 3 Haiku as the default model for examples and exercises to minimize learner API costs while still demonstrating full capabilities. Course materials explicitly discuss trade-offs between model capability and cost, helping developers make informed decisions about model selection for their applications.

Delivers course content through executable Jupyter notebooks that combine explanatory text, code examples, and live API calls to Claude, enabling learners to modify prompts and immediately observe output changes. This interactive format creates tight feedback loops where learners can experiment with techniques, see results in real-time, and build intuition through hands-on exploration rather than passive reading.

Teaches techniques for breaking complex tasks into sequences of simpler prompts that build on each other's outputs, enabling more reliable and interpretable multi-step reasoning. The course covers prompt chaining patterns, managing context across chain steps, and strategies for handling failures or unexpected outputs in intermediate steps.