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Prompt Engineering for Engineers
Write prompts that actually work in production — reliable, testable, and cost-efficient.
CREATED BY
P
Punit D. [PLACEHOLDER] ★ 5.0
Senior Product Designer at [BRAND] | 7+ years of experience
About this Path
For developers who have used LLMs but want rigorous, repeatable techniques. Covers the mental model behind transformer behaviour, all major prompting patterns, structured output extraction, and how to test prompts like code. You will end with a personal prompt library and an automated eval workflow you can drop into any project.
Path Overview
Intermediate LevelCertificate of CompletionAbout 28 hours to completeEnglish language10+ curated videosLearn online at your own pace4 modules with resourcesGamified & interactive
Path Curriculum
Tokens, Attention, and Next-Token Prediction
Intuition for why word order, delimiters, and role tags change model behaviour.
System vs User vs Assistant Roles
When and how the three-turn format steers instruction following and persona.
Context Window Budget and Token Counting
Estimate token cost with tiktoken; prioritise content when context is tight.
Zero-Shot and Few-Shot Prompting
Craft minimal examples that generalise; pick exemplars that cover edge cases.
Chain-of-Thought and Step-Back Prompting
Trigger multi-step reasoning; reformulate hard questions before answering.
Self-Consistency and Majority Voting
Sample multiple reasoning paths and aggregate to improve factual accuracy.
ReAct and Tool-Use Patterns
Structure thought-action-observation loops for agents that call external APIs.
Function Calling and JSON Mode
Force schema-valid JSON output using OpenAI function calling and Anthropic tool use.
Output Parsers with LangChain and Instructor
Parse and validate LLM output into Pydantic models with automatic retry.
Prompt Templates and Version Control with Langfuse
Parameterise prompts, version them, and deploy without redeploying code.
Prompt Evaluation with LLM-as-Judge
Write judge prompts that score correctness, tone, and completeness automatically.
Regression Testing Prompt Changes in CI
Catch regressions by running evals against a golden dataset on every commit.
Prompt Injection and Jailbreak Mitigations
Sandwich pattern, input sanitisation, and output validation to block adversarial inputs.
Cost and Latency Profiling
Identify expensive prompt segments; apply caching and model downgrade strategies.
What you'll learn
- ✓Explain why prompt phrasing affects model output using the attention and token probability framework.
- ✓Apply zero-shot, few-shot, chain-of-thought, and self-consistency patterns to the right problem class.
- ✓Extract structured JSON from LLM output reliably using function calling, tool use, and output parsers.
- ✓Design and run a prompt evaluation suite using LLM-as-judge and reference-based metrics.
- ✓Reduce token cost and latency by trimming system prompts, batching, and using caching headers.
- ✓Identify and mitigate prompt injection, jailbreaks, and over-refusal failure modes in production.