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Claude Essentials for Developers
Duration
2 Days
Course
AICL-100A
Available Formats
Classroom Training, Online Training
Course Description
Overview
This Claude Essentials for Developers course is designed to give software developers a fast, practical grounding in Claude: what it is, how it reasons, how to call it, and how to build reliable behavior into real code. Over two days of hands-on instruction, participants move from first principles through working API integrations, tool use, and the prompt design patterns that separate demo-quality output from production-ready results. The course is built for developers who need to be productive with Claude immediately and who may continue toward deeper agentic and certification-track training.Day 1 builds the mental model and gets developers making real API calls with reliable prompting patterns. Day 2 moves into the capabilities that turn a working API call into the foundation of an application: streaming, multi-turn context management, tool use, structured output, and the verification discipline required for production code.
Objectives
Upon completion of the Claude Essentials for Developers course, students will be able to:
- Explain what Claude is, how large language models generate output, and what that mechanism means for reliability in software systems
- Authenticate to the Claude API and make working requests in Python or JavaScript
- Apply core prompting patterns — role framing, explicit criteria, and few-shot examples — to produce consistent, reliable outputs
- Implement streaming responses and handle partial output appropriately
- Use tool use to extract structured output that conforms to a defined schema
- Manage multi-turn conversations and context effectively within the model's context window
- Identify hallucination risks in their own code paths and design verification steps appropriate to the stakes
- Evaluate which development tasks are good candidates for Claude integration and which are not
Audience
Software developers, backend and full-stack engineers, and technical professionals who need working proficiency with Claude for application development and are new to the Claude ecosystem
Prerequisites
-
Working proficiency in Python or JavaScript and experience consuming REST APIs. Familiarity with JSON. No prior experience with Claude or other LLMs required.
Topics
- What Claude Is and How It Works
- Large language models as next-token predictors, not reasoning engines
- How Claude differs from other AI tools developers may have encountered
- Hallucination: what it is, why it is structural rather than a bug, and what that means for code design
- The Claude model family and selecting the right model for a task
- Your First Claude API Calls
- Authentication, request structure, and response handling
- Model selection and parameter tuning: temperature, max tokens, and stop sequences
- Error handling, rate limits, and production reliability basics
- Hands-on lab: build a working Claude API integration in your language of choice
- Prompting Patterns That Produce Reliable Output
- Role and system prompts: setting the frame for the model
- Explicit criteria versus vague instructions: why precision matters
- Few-shot prompting: teaching format and behavior through examples
- Common prompting mistakes and how to diagnose them
- Day 1 Synthesis
- Applying the day's patterns to a realistic developer task end-to-end
- Setting up for Day 2: from single calls to real application behavior
- Streaming and Response Handling
- Implementing streaming responses in application code
- Handling partial outputs, stop reasons, and interruption
- When to stream and when to wait for the full response
- Multi-Turn Conversation and Context Management
- Structuring multi-turn conversations in API calls
- The context window: what fits, what doesn't, and how to manage it
- When to start a new conversation versus extend an existing one
- Tool Use and Structured Output
- What tool use is and how Claude decides when to call a tool
- Defining tools with JSON schemas for guaranteed output structure
- Reading tool calls and returning tool results correctly
- Hands-on lab: extract structured data from unstructured input using tool use
- Verification, Reliability, and Responsible Integration
- Designing verification steps for AI-generated output in production code
- Identifying tasks that should be automated versus those that need a human in the loop
- What to log, what to monitor, and how to catch failures early
- From Foundation to Practice
- Mapping what you've learned to real development tasks in your own work
- Where Claude fits — and where it doesn't — in typical application architectures
- Next steps: what deeper capabilities build on this foundation
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Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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