Prompts are code. This course teaches you how to write, version, and test prompts that produce consistent results across model updates — covering chain-of-thought, few-shot examples, output formatting, and common failure modes.
Prerequisites
- Basic familiarity with an LLM API (OpenAI, Anthropic, or similar)
- Identify the components of a well-structured prompt
- Apply zero-shot, one-shot, and few-shot patterns appropriately
- Use chain-of-thought to improve reasoning accuracy
- Constrain and validate model output format
- Version prompts and test them against a regression suite
Step 1: The Anatomy of a Good Prompt
A well-structured prompt has a role definition, clear task instruction, relevant context, output format spec, and (optionally) examples. Each element reduces the model's degrees of freedom and makes the output more predictable.
Step 2: Zero-Shot vs. Few-Shot Patterns
Zero-shot works for straightforward tasks where the model already has strong priors. Add few-shot examples when the task has an unusual format, a subtle distinction the model regularly misses, or domain-specific terminology that changes the expected output shape.
Step 3: Chain-of-Thought and Reasoning Traces
Asking the model to "think step by step" before answering improves accuracy on multi-step reasoning tasks. For production use, separate the reasoning trace from the final answer so you can validate the answer independently without parsing the full trace.
Step 4: Controlling Output Format
Specify the exact format you need — JSON schema, markdown section headers, numbered list — and validate the output programmatically. Don't rely on prose descriptions alone; a concrete example in the prompt is more reliable than a natural-language format description.
Step 5: Testing and Versioning Prompts
Treat prompts as code: store them in version control, write an eval suite against them, and require a passing eval run before merging a prompt change. Use a dedicated prompts directory and name files after the task, not the model.
Step 6: Handling Edge Cases and Jailbreaks
Add an adversarial slice to your eval suite that covers known jailbreak patterns and edge-case inputs. Use a system prompt to define boundaries clearly. For high-stakes applications, add an output classifier as a second layer of defence rather than relying solely on the generation prompt.
Next Mini Course
Prompt Engineering for Agents – Tool Use and Multi-Step Reasoning – Move from single-turn prompts to agentic workflows: design prompts that reliably invoke tools, handle multi-step reasoning chains, recover from errors, and stay on-task across long contexts.
Further Reading
- Anthropic Prompt Library – Curated collection of production-ready prompts
- Prompting Guide – Comprehensive techniques and research summaries
- DSPy – Framework for programmatic prompt optimisation
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Overview
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Learning Outcomes
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Steps
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Completion Checklist