Building AI Products with LLMs

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Build AI products with LLMs

Ship LLM features that survive contact with real users, not just demo gifs.

Your demo works on three prompts and falls apart on the fourth. Learn the patterns that make LLM features actually hold up once real users start typing into them.

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Overview

The hard part of LLM products isn't the prompt. It's the second week, when your demo meets messy users, weird inputs, and a CFO asking what it costs. This course is the engineering-flavoured guide to going from prototype to production, covering the trade space (model choice, eval, latency, cost, fallback) the way teams who've actually shipped think about it.

What you'll learn

By the end, you'll be able to do these, not just have read about them.

  • Architect LLM-powered features that survive real users and real load

  • Pick the right model, prompt pattern, and orchestration for each job

  • Set up evals, observability, and rollback so you can ship with confidence

  • Reason about cost, latency, and quality as a single system

Who this is for

  • You're an engineer or PM-engineer who's prototyped with an LLM and now needs to ship it for real.

  • You're tired of LinkedIn-grade GenAI advice and want the engineering substance.

  • You're an applied AI engineer at a startup carrying the AI feature surface alone.

Prerequisites

  • You can write a small backend service and call an API.

  • You've used a frontier LLM at least once, even just through ChatGPT or Claude.

Suggested chapters

This is the typical chapter list. Your version is generated against your background and adapts as you go. It may compress, expand, or reorder these.

  1. 01

    The product, not the prompt

    Framing AI features around real user jobs and the failure modes they tolerate.

  2. 02

    Picking your model

    Frontier vs open vs small, the matrix of capability, latency, cost, and privacy.

  3. 03

    Prompts that hold up

    Structure, decomposition, and meta-prompts, patterns that survive product changes.

  4. 04

    Retrieval that works

    When to RAG, when to fine-tune, when to do neither. Embeddings, chunking, hybrid search.

  5. 05

    Evals as your foundation

    Golden sets, LLM-as-judge, regression suites, the discipline that separates real teams.

  6. 06

    Latency, cost, caching

    Streaming, prompt caching, model routing, partial responses, the production toolkit.

  7. 07

    Failure & guardrails

    Fallbacks, refusal, prompt-injection defense, rate limits, abuse handling.

  8. 08

    Capstone: ship a feature

    Take one real LLM feature from prompt-stage to a production-shaped build with evals and budgets.

Real-world projects

  • 01Build a small RAG over your team's actual docs and measure retrieval quality.
  • 02Stand up an eval suite that fails CI when prompt quality regresses.
  • 03Design and instrument a fallback path for when your primary model is slow or out.
  • 04Ship one end-to-end LLM feature with budgets, evals, and observability wired in.

Tools & concepts

Real tools and ideas covered. Octo brings them in when they fit your stack.

  • OpenAI API
  • Anthropic Claude
  • Open-weights models
  • Embeddings
  • Vector databases
  • RAG
  • Prompt caching
  • LLM-as-judge
  • Streaming
  • Function calling
  • Evals frameworks
  • Observability

Where this leads

  • 01

    Applied AI / ML engineer roles building product-facing LLM features.

  • 02

    Engineer who can credibly own the AI surface at a small or mid-stage startup.

  • 03

    Foundation for advanced topics, agents, fine-tuning, multimodal pipelines.

Common questions

  • Do I need ML background?

    No. The course is engineering-flavoured, it assumes you can write a service and call APIs, not that you trained a model from scratch.

  • Will I learn how transformers work internally?

    Only as much as a working engineer needs. If you want pretraining/architecture depth, take 'LLMs & Foundation Models' instead.

  • Is this a fixed course, or is it built for me?

    Built for you. The chapter list below is a typical outline. Your actual course is generated against your role, experience, and what you already know, then adapts as you go.

  • How long does it take?

    Most learners finish in 2–6 weeks at a normal pace, depending on the topic. Octo compresses where you're strong and slows down where you're weak.

  • Is there a fixed schedule or cohort?

    No. You start when you start. There's no live session, no calendar, no deadline.

  • Can I ask questions while I'm learning?

    Yes, every module has an AI Sidekick in the margin. Ask for a different example, push back, or get a clarifying analogy without leaving the page.

  • What do I get at the end?

    A verifiable, HMAC-signed certificate with a public verify page. It records the modules passed, scores, and capstone, not just attendance.

  • How much does it cost?

    Octo is in research preview, courses are open. We'll be transparent before pricing changes.

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