Claude Prompt Engineering
Design reliable Claude prompts using templates, variables, and console tooling to keep instructions consistent while scaling experimentation.
Why take this course?
Build prompts that stay consistent as your app grows. Learn how to separate fixed instructions from dynamic context, wire up Claude Console tooling, and ship repeatable prompt patterns.
Course Modules
Situates prompt engineering within the Claude workflow, clarifying prerequisites, when to use it versus other levers, and the key techniques covered in the series.
Learning Goals
- Recognize the prerequisites needed before iterating on prompts effectively.
- Decide when prompt engineering is the right lever versus model, latency, or cost changes.
- Identify the primary Claude prompt engineering techniques and supporting console tools.
Explains how to separate fixed and variable content in Claude prompts, use double-brace placeholders, and manage templates for consistency, testing, and scale.
Learning Goals
- Differentiate fixed instructions from variable context in Claude prompts.
- Use double-brace placeholders to represent dynamic inputs in templates.
- Explain when and why to standardize prompts with templates for consistency, testing, and version control.
- Apply a basic template to a simple translation example using variables.
Shows how to make prompts explicit and contextual so Claude behaves like a new teammate with concrete instructions, including side-by-side examples of vague vs. specific prompts.
Learning Goals
- Explain why Claude needs explicit context and stepwise instructions to follow tasks reliably.
- Provide contextual details such as audience, workflow, and success criteria to reduce hallucinations or filler.
- Write prompts that specify exact outputs, constraints, and formatting expectations.
- Contrast unclear versus clear prompts using structured examples and tables.
Teaches how to steer Claude with few-shot examples, emphasizing relevance, diversity, and structure to improve accuracy and consistency.
Learning Goals
- Describe how few-shot/multishot examples guide Claude toward the desired format and style.
- Select relevant and diverse examples, including edge cases, to reduce unintended patterns.
- Structure examples with <example> (and <examples>) tags to make patterns explicit.
- Apply 3–5 examples to a real task to boost output consistency and reduce misinterpretation.
Covers chain-of-thought prompting to improve complex reasoning by giving Claude structured space to think, with basic, guided, and structured patterns plus before/after examples.
Learning Goals
- Explain when chain-of-thought (CoT) helps and when to avoid it due to latency or verbosity.
- Prompt Claude to think step-by-step for math, logic, research, or multi-factor decisions.
- Structure CoT with guided steps or XML tags (e.g., <thinking>, <answer>) to separate reasoning from final output.
- Compare outputs with and without CoT to show depth, coherence, and debuggability improvements.
Shows how to segment instructions, context, and examples with XML tags to boost clarity, accuracy, and post-processing, with before/after examples.
Learning Goals
- Explain why XML-tagged prompts reduce ambiguity and misinterpretation.
- Apply consistent, nested tags to separate instructions, examples, formatting, and context.
- Combine XML tags with multishot prompting and chain of thought for structured reasoning.
- Transform unstructured prompts into tagged versions that preserve tone and format requirements.
Covers role prompting via the system parameter to shape expertise, tone, and focus, with side-by-side examples showing impact on legal and financial analysis.
Learning Goals
- Explain how system-role prompting shifts Claude into a domain persona for accuracy and tone.
- Place role definition in the system prompt while keeping task instructions in the user turn.
- Experiment with different roles to surface varied insights on the same data.
- Compare outputs with and without role prompting to validate improvements.
Covers using assistant-prefill to steer Claude's output, skip preambles, enforce formats, and maintain character, with warnings about whitespace and mode limitations.
Learning Goals
- Explain how assistant-prefill guides Claude's continuation and when it is supported.
- Use prefilling to skip boilerplate and force structured outputs like JSON.
- Combine prefilling with role prompting to maintain character consistency.
- Avoid pitfalls like trailing whitespace and extended-thinking mode limitations.
Breaks complex tasks into chained prompts for accuracy, clarity, and debuggability, with examples for self-correction, legal review, and strategy analysis.
Learning Goals
- Explain why chaining prompts improves accuracy and traceability on multi-step tasks.
- Design sequential subtasks with single-task goals and clear handoffs (e.g., XML tags).
- Use self-correction chains to review and refine outputs for high-stakes work.
- Apply chaining to real workflows like legal reviews, research summaries, and strategy documents.
Guides using Claude's extended context effectively: ordering long inputs, structuring documents with XML, and grounding answers in quotes for accuracy.
Learning Goals
- Position longform inputs near the top of prompts to improve relevance on large contexts.
- Structure multi-document prompts with XML tags for content and metadata.
- Ground responses by extracting and quoting relevant spans before analysis.
- Apply these patterns to reduce noise when working with 200K-token contexts.
Advanced strategies for extended thinking mode: budgets, batching, prompting patterns, and examples that benefit from longer reasoning.
Learning Goals
- Configure thinking budgets safely and know when to batch long runs.
- Use high-level guidance first, then tighten instructions based on thinking output.
- Combine multishot patterns and structured frameworks with extended thinking.
- Apply reflection and self-checks to improve consistency on long reasoning tasks.