AI Integration & Prompt Engineering
Build production LLM applications — RAG, agents and evaluation, not just clever prompts.
Lifetime access · Certificate included · US timezone sessions
The 30-minute demo is free, needs no card, and carries no obligation. If we aren't the right fit for your goals, we'll tell you.
- Tutorials finished, but nothing deployed you can show
- A resume that lists coursework instead of shipped work
- Freezing when an interviewer asks you to explain a decision
- Unsure which skills US employers actually screen for
- 4 deployed projects with real source code
- A US-format resume built around what you shipped
- Mock interviews with written feedback, before the real one
- A clear path toward AI Engineer
Who this course is for
This course was designed around specific situations. If one of these sounds like you, you're in the right place.
What you'll learn
By the end of this course you will be able to do each of the following in real projects, not just in exercises.
Design prompts that are reliable across inputs, not just impressive in demos
Build retrieval-augmented generation pipelines that cite their sources
Choose and tune chunking, embedding and reranking strategies
Implement tool-calling agents with proper guardrails
Evaluate LLM output systematically and control cost and latency
Your learning roadmap
The course runs in phases. Each one ends with a concrete milestone, so you always know whether you're on track.
- 1
LLM Fundamentals & Prompting
Weeks 1–2Focus: Working with models
What you'll do
- Understand tokens, context windows, temperature and sampling
- Write structured prompts with clear roles and output schemas
- Use few-shot examples and chain-of-thought appropriately
- Force reliable structured output (JSON, tool schemas)
MilestoneA prompt suite that produces valid structured output across 100 varied inputs.
- 2
RAG & Vector Search
Weeks 3–5Focus: Grounding models in your data
What you'll do
- Chunk documents in ways that preserve meaning
- Generate and store embeddings in a vector database
- Implement hybrid search and reranking
- Cite sources and handle 'I don't know' correctly
MilestoneA RAG system answering questions over a real document corpus with citations.
- 3
Agents & Tool Use
Weeks 6–7Focus: Letting models act
What you'll do
- Design tool-calling schemas that models use correctly
- Build multi-step agent loops with termination guarantees
- Add guardrails, validation and human-in-the-loop checkpoints
- Handle failure modes: loops, hallucinated tools, runaway cost
MilestoneAn agent that completes a multi-step task safely and predictably.
- 4
Evaluation & Production
Week 8Focus: Shipping responsibly
What you'll do
- Build evaluation sets and LLM-as-judge pipelines
- Measure and reduce cost, latency and token usage
- Add caching, streaming and fallback models
- Monitor quality regressions after prompt or model changes
MilestoneA deployed AI feature with an automated evaluation suite and cost dashboard.
Everything you'll cover
A map of the whole curriculum at a glance — every major area and the topics inside it.
- Tokens
- Context windows
- Temperature
- Streaming
- Model selection
- Structured output
- Few-shot
- Chain-of-thought
- System prompts
- Prompt injection
- Chunking
- Embeddings
- Vector DBs
- Hybrid search
- Reranking
- Citations
- Tool calling
- Planning loops
- Guardrails
- Human-in-the-loop
- Termination
- Eval sets
- LLM-as-judge
- Regression testing
- Human review
- Caching
- Cost control
- Latency
- Fallbacks
- Monitoring
Week-by-week syllabus
A structured breakdown of exactly what you'll cover, week by week.
Projects you'll build
You finish with a portfolio of deployed work — the thing hiring managers actually look at.
Document Q&A with Citations
A RAG system over a real corpus that answers with linked sources and admits uncertainty.
Structured Extraction Pipeline
Convert unstructured documents into validated JSON at scale with error handling.
Tool-Using Agent
An agent that queries APIs and databases to complete multi-step tasks safely.
Evaluated Production Feature
A deployed AI feature with automated evals, caching and cost monitoring.
Where this course can take you
Typical roles this course prepares you for, with current US market compensation ranges.
| Role | US salary range | Demand |
|---|---|---|
| AI Engineer | $125,000 – $185,000 | Extremely high |
| LLM Application Developer | $120,000 – $175,000 | Extremely high |
| AI Product Engineer | $115,000 – $170,000 | Very high |
| AI Solutions Consultant | $110,000 – $165,000 | High |
Salary ranges are indicative market figures for reference, not guarantees of employment or compensation.
Prerequisites
- Comfortable writing Python or TypeScript
- Basic understanding of REST APIs
- No machine learning background required — this is applied, not research
Tools you'll use
Frequently asked questions
What is RAG and why does every AI application use it?
Retrieval-augmented generation retrieves relevant documents from your own data and supplies them to the model as context before it answers. It is the standard solution to two problems: models do not know your private data, and they invent answers when they lack information. RAG grounds responses in real sources you can cite.
Is prompt engineering a real skill or a passing trend?
Writing a single clever prompt is not a career. Designing prompt systems that stay reliable across thousands of varied inputs, produce valid structured output, resist injection and can be regression-tested is genuine engineering — and that is what this course teaches.
Do I need machine learning experience for this course?
No. This is applied integration engineering, not model training. If you can write Python or TypeScript and call a REST API, you have the prerequisites. Our Machine Learning course is a separate track for people who want to build models themselves.
Which AI provider does the course use?
The course works across major providers including Anthropic's Claude and OpenAI's models, because production systems routinely use more than one and need fallbacks. The patterns taught are deliberately provider-agnostic.
How do you stop an LLM application from hallucinating?
You cannot eliminate it, but you can control it: ground responses in retrieved context, require citations, constrain output schemas, add validation layers, and build evaluation suites that catch regressions. The course treats this as an engineering problem with measurable outcomes rather than a prompt trick.