Learn to build software on top of language models — API calls, function calling, streaming, evals, and the engineering judgment to ship features that hold up.
The best way to learn LLM app development is to ship progressively harder features against a real model API — completions, then structured output and function calling, then streaming, then evals — because the skill is engineering around a non-deterministic component, and that only comes from building. LearnAI turns this into a structured course with an AI tutor that reviews your code and design decisions. Free to start, no account needed.
Building with LLMs is a distinct engineering discipline. The API call is trivial; everything around it is the job: prompts that live in version control, structured outputs your code can parse, function calling that connects models to your systems, streaming UX, cost and latency budgets, and — the part that separates production teams from demo teams — evaluation that tells you whether any of it actually works. Traditional software instincts help, but a non-deterministic component in the middle of your stack changes how you design, test, and ship.
This course teaches that discipline end to end. You'll work directly with model APIs from the first module, then layer on the production concerns in the order they bite: structured output, tool use, streaming, error handling, evals, and cost control. Your AI tutor explains the patterns, reviews your implementations, and pushes you past 'it worked when I tried it' toward 'I can show it works.' You finish having shipped a real LLM-powered feature with an eval suite behind it.
7 weeks at 4 hours per week · built by LearnAI, adjusted to your level and goals
This is an example of the course plan LearnAI generates — yours will be personalized from your first message.
First principles hands-on — make calls to a model API, understand the request anatomy, and build a small working tool by week's end.
Treat prompts like the production assets they are — templating, versioning, and designing prompts your application logic can depend on.
Get reliable JSON out of a probabilistic system — schemas, validation, and retry patterns that turn model output into data your code trusts.
Connect models to your systems — define tools, handle the call loop, and build a feature where the model queries real data.
Make it feel right — stream tokens to the client, handle errors and timeouts mid-stream, and learn the UX patterns users now expect from AI features.
The discipline that separates professionals — build an evaluation suite for your feature, from golden test sets to LLM-as-judge, and wire it into your dev loop.
Put it in production shape — logging, cost controls, model selection, and a capstone where you ship a complete evaluated LLM feature.
LLM application engineering is among the most sought-after developer skills in 2026 hiring — companies across every sector are adding AI features to existing products, and the bottleneck is engineers who can do it well. Notably, the demand is not for ML researchers: it's for product engineers who can integrate model APIs, design around failure modes, and evaluate quality. That's a learnable skill for any working developer.
The skill also compounds unusually well right now. The core patterns — API integration, structured output, function calling, evals — are the foundation for everything downstream: agents, RAG systems, AI-native products. Models and SDKs will keep changing, but the engineering discipline of building reliably on top of a probabilistic component is durable, and developers who learn it early keep collecting the dividend.
You write real code against real APIs, and the tutor reviews it — flagging missing error handling, brittle parsing, and prompt patterns that will break under real input. It's the feedback loop of a senior teammate who's shipped LLM features.
Experienced backend developers skip API basics and dig into evals and architecture; newer developers get more scaffolding on the fundamentals. The course calibrates from your first conversation.
Cost, latency, failure handling, and evaluation aren't a final chapter — they're woven through every build, because the gap between demo and production is exactly what employers are hiring for.
Finish the modules and capstone and pass the reviews, and Pro members earn a LearnAI completion certificate — with a shipped, evaluated LLM feature to show alongside it.
Comfortable working knowledge of at least one language — Python or JavaScript/TypeScript are ideal since SDKs and examples favor them. You should be able to write functions, call APIs, and handle JSON without hand-holding. You do not need any machine learning background: this is application engineering on top of models, not model training. Non-programmers should start with our AI Automation or no-code tracks instead.
Only at the working level, which the course provides: tokens, context windows, sampling, and why outputs vary. You don't need transformer architecture or training theory to ship excellent LLM features, the same way you don't need database internals to design good schemas. What you do need — and what most self-taught builders skip — is evaluation discipline, which gets a full module.
Very little if you're deliberate. Course exercises use small models and short contexts wherever possible, and cost-awareness is taught as a first-class skill — expect single-digit dollars across the whole course for most learners. The tutor helps you set up spend limits before your first call, which is itself a production habit worth having.
Direct APIs first — that's how this course teaches, because frameworks make more sense once you understand what they're abstracting. After the fundamentals, the course surveys the framework landscape so you can adopt one (or not) with judgment. In 2026 hiring, understanding the underlying patterns reads far stronger than framework familiarity alone.
Pro members earn a LearnAI completion certificate on finishing. Candidly: it's not an accredited or vendor credential like AI-900. But for application development roles, vendor certs test recall while hiring managers test building — the capstone feature and eval suite you produce here are the evidence that matters, and the certificate is the shareable record that you built them in a structured program.
Free to start, genuinely: no account, no card, straight into module one. The free tier's per-course message allowance covers a real chunk of the material, but a code-review-heavy course burns messages faster than most — Pro removes the cap and adds the certificate, and this is the course where that trade is most often worth it.
By around week four you'll have built structured-output and function-calling features — legitimately resume-worthy with honest framing. The full seven weeks gets you the piece that holds up in interviews: an evaluated, shipped feature and the ability to explain why it's reliable, which is the question every LLM engineering interview eventually asks.
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