About Prompt Engineer
Prompt engineering is the craft of getting reliable, useful results out of large
language models, and this course walks through it in the order most people actually
learn it. It starts with What Is Prompt Engineering (how LLMs work,
tokens, and context windows), then Prompting Basics and
Few-Shot Prompting & Examples so you can write clear instructions
and teach a model by showing it.
Next comes Reasoning Techniques: chain-of-thought, self-consistency,
and prompt chaining for harder problems. From there,
Structured Outputs & Formatting covers JSON, XML tags, and
reusable templates, while Model Parameters & Selection covers
temperature, top-p, and choosing the right model for the job.
RAG & Context Engineering covers embeddings, vector databases,
and grounding answers in your own data, and
Tools, Function Calling & Agents covers letting a model take
actions across several steps.
Finally, Evaluating & Iterating Prompts covers test sets,
rubrics, and prompt versioning, and Safety, Security & Production
covers prompt injection, guardrails, and keeping cost and latency in check: the skills
that separate a clever demo from a prompt that works every time.
Content
10 modules
01
What Is Prompt Engineering
What a prompt engineer does, how large language models work, and why tokens, context windows, and training shape every response.
LLMsTokensContext Window
02
Prompting Basics
Writing clear instructions with context, a role, and a requested format, and iterating from a first draft to a reliable prompt.
Zero-ShotSystem PromptsRoles
03
Few-Shot Prompting & Examples
Teaching a model by showing it: choosing, ordering, and formatting examples to steer tone, style, and structure.
Few-ShotOne-ShotExample Selection
04
Reasoning Techniques
Getting better answers on harder problems with step-by-step reasoning, self-consistency, and breaking tasks into smaller prompts.
Chain-of-ThoughtSelf-ConsistencyPrompt Chaining
05
Structured Outputs & Formatting
Getting predictable, machine-readable responses with JSON, XML tags, schemas, and reusable prompt templates.
JSONXML TagsPrompt Templates
06
Model Parameters & Selection
Tuning temperature, top-p, and output length, and choosing the right model for a task by quality, speed, and cost.
TemperatureTop-PModel Selection
07
RAG & Context Engineering
Grounding answers in your own data with retrieval-augmented generation, and managing what goes into the context window.
RAGEmbeddingsVector Databases
08
Tools, Function Calling & Agents
Letting a model call functions and external tools, and designing agents that plan and act over several steps.
Function CallingAgentsMCP
09
Evaluating & Iterating Prompts
Measuring prompt quality with test sets, rubrics, and A/B comparisons, and versioning prompts so changes can be tracked.
EvalsLLM-as-JudgePrompt Versioning
10
Safety, Security & Production
Defending against prompt injection and jailbreaks, adding guardrails, and keeping cost, latency, and reliability under control.
Prompt InjectionGuardrailsCost & Latency
What You Can Do After This Course
By the end of the curriculum you'll have worked through 5 hands-on projects and
covered the core of what a junior to mid-level prompt engineering role expects. Concretely,
you'll be able to:
Explain how LLMs work: what tokens, context windows, and training mean for the answers a model gives, and where it tends to go wrong.
Write clear prompts: give a model the right instructions, context, role, and format, and refine a draft into a dependable prompt.
Teach with examples: use few-shot prompting to control tone, style, and structure without any fine-tuning.
Improve reasoning: apply chain-of-thought, self-consistency, and prompt chaining to multi-step problems.
Get structured output: request JSON or XML that code can parse, and build reusable prompt templates.
Ground answers in data: set up retrieval-augmented generation and manage the context window deliberately.
Build, test, and protect: connect models to tools and agents, evaluate prompts against test sets, and defend against prompt injection before shipping.