A written version of the interactive roadmap above — every station, what you'll learn, and a small thing to build — laid out for reading, reference and search.
Foundations Start here
F1. What is Generative AI
Beginner · 30 min
A discriminative model tells cat from dog; a generative model paints a cat that never existed. Generative AI learns the underlying distribution of data — text, images, audio — and samples new examples from it. That one shift is what powers ChatGPT, Midjourney, and every model that creates.
Skills: Generative vs discriminative · Learning a distribution · Sampling new data · Modalities: text / image / audio
Build it: List five products you use that generate content. For each, name the modality and whether it feels more like autocomplete or like painting from noise.
F2. How LLMs Generate Text
Beginner · 60 min
Text generation is next-token prediction on a loop: the model reads everything so far and picks the most likely next token, then repeats. Under it is the Transformer and self-attention. Understanding this makes prompting, streaming, and context limits all click.
Skills: Next-token prediction · Self-attention · Autoregressive generation · Streaming output
Build it: Generate the same prompt at temperature 0 and 1.0 several times. Explain, in terms of next-token probabilities, why one is repetitive and one is wild.
F3. How Diffusion Generates Images
Beginner · 60 min
Image models work backwards from noise. In training they add noise to real images until they are static; at generation they start from pure noise and a trained network removes it step by step, sculpting a picture. That is the idea behind Stable Diffusion, DALL·E, and Midjourney.
Skills: Forward noising · Reverse denoising · Sampling steps · Text conditioning
Build it: Explain to a friend why "add noise, then learn to remove it" is easier to train than generating a full image in one shot.
F4. Embeddings & Multimodal
Intermediate · 45 min
To connect words and images you put both in one shared space, where a photo and its caption land close together. That is what CLIP does — and it is the bridge that lets a text prompt steer an image model. Multimodal generation lives on this idea.
Skills: Shared embedding space · Image–text alignment (CLIP) · Cosine similarity · Text-to-image guidance
Build it: Explain how "a photo of a cat" can steer an image generator even though the model was never told what a cat looks like in labels.
F5. Prompting for Generation
Beginner · 45 min
A prompt is the steering wheel of a generative model. For text: roles, structure, examples. For images: subject, style, composition, and negative prompts. Learning to describe what you want — and what you do not — is the core creative skill of GenAI.
Skills: Descriptive prompts · Negative prompts · Style & structure control · Iteration
Build it: Take one image idea and write three prompts: bare, detailed, and detailed-with-negatives. Compare how much control each gives.
F6. Tokens, Context & Cost
Intermediate · 30 min
Generation is metered. Text is billed per token and bounded by a context window; images cost per generation and per resolution/steps. Knowing what drives cost and latency is what separates a fun demo from a product you can actually afford to run.
Skills: Tokenization · Context windows · Cost per generation · Latency drivers
Build it: Estimate the cost of generating 1,000 product descriptions vs 1,000 images. Which is pricier, and what knob would you turn to cut it?
Techniques Level up
T1. Text Generation & Sampling
Intermediate · 45 min
How a model picks the next token shapes everything. Temperature scales randomness, top-p and top-k prune the candidates, and beam search hunts for the most likely sequence. Tuning these trades creativity against reliability — the dial every text feature needs set right.
Skills: Temperature · Top-p / top-k · Beam search · Creativity vs reliability
Build it: For a legal-summary tool and a brainstorming tool, pick temperature and top-p for each and justify why they differ.
T2. Image Generation
Intermediate · 60 min
Modern image models run diffusion in a compressed latent space for speed, guided by your prompt. Guidance scale controls how hard it follows the text; ControlNet-style conditioning adds pose, depth, or edges so you can direct composition, not just describe it.
Skills: Latent diffusion · Guidance scale · ControlNet conditioning · Img2img & inpainting
Build it: Explain what raising the guidance scale does to an image, and when a lower value actually produces a better result.
T3. Multimodal Models
Advanced · 60 min
Vision-language models take an image and text together and reason about both — describing a photo, answering questions about a chart, reading a screenshot. They fuse a vision encoder with a language model, and they are the fastest-moving frontier of GenAI.
Skills: Vision-language models · Image + text in · Visual question answering · Grounding to pixels
Build it: List three product features that only become possible once a model can see an image and read text at the same time.
T4. Audio & Video Generation
Advanced · 45 min
The same generative ideas extend to sound and motion: text-to-speech that sounds human, music from a prompt, and the young, fast-improving field of generated video. These push the hardest on compute, consistency over time, and evaluation.
Skills: Text-to-speech · Music generation · Text-to-video · Temporal consistency
Build it: Why is generating a 10-second video far harder than generating 10 separate images? Name the constraint that ties the frames together.
T5. Fine-Tuning Generative Models
Advanced · 60 min
To teach a model your style, brand, or a specific subject, fine-tune it. LoRA trains tiny adapters cheaply; DreamBooth teaches an image model a new subject from a handful of photos. The skill is knowing when a few example prompts would do instead.
Skills: LoRA adapters · DreamBooth · Style vs subject tuning · When not to fine-tune
Build it: You want a model to always draw your mascot. Argue whether prompting, LoRA, or DreamBooth fits — and what data each needs.
T6. RAG & Grounded Generation
Intermediate · 60 min
Left alone, generative models confidently make things up. Grounding them in retrieved sources — RAG — keeps generated text tied to real documents you control, so answers are checkable and citable. It is the main defence against hallucination in generative products.
Skills: Retrieval-augmented generation · Grounding · Citations · Hallucination control
Build it: Design a "chat with our help docs" feature. How do you make sure every answer is grounded in a real doc, with a link?
Production Ship it
P1. Evaluating Generative Output
Advanced · 90 min
How do you score something open-ended — a paragraph, an image? There is no single right answer. You combine human review, automated metrics, and LLM-as-judge into a repeatable eval so you can tell whether a change actually improved quality.
Skills: Human evaluation · Automated metrics · LLM-as-judge · Golden sets & regression
Build it: Design an eval for an image generator: what mix of human ratings and automated checks tells you a new model version is better?
P2. Guardrails & Content Safety
Intermediate · 60 min
Generative models can be prompted into unsafe or off-brand output. Safety is layered: filter incoming prompts, moderate generated text and images, block disallowed content, and handle refusals gracefully — especially critical for anything public-facing.
Skills: Prompt & output filtering · Media moderation · Disallowed content · Refusal handling
Build it: For a public image generator, list the checks you run before showing a result to a user, and what happens when one trips.
P3. Serving & Latency
Advanced · 60 min
Generation is slow and bursty. Streaming tokens as they are produced hides latency; batching improves throughput; and long generations or high-resolution images dominate cost. Serving GenAI well is about making the wait feel short and the bill stay small.
Skills: Token streaming · Batching · Time-to-first-token · Long-generation cost
Build it: Why does streaming make a 20-second answer feel fast? And what does batching buy you that streaming does not?
P4. Cost & Caching
Intermediate · 45 min
Repeated or similar generations are everywhere — cache them. Route simple requests to smaller, cheaper models, cache identical prompts, and cap spend per user. In generative products, cost control is a feature users never see but the business always feels.
Skills: Prompt / result caching · Model routing · Spend caps · Cheap-model fallbacks
Build it: Design a cache for a text generator: when is a cached result safe to reuse, and when must you regenerate?
P5. Watermarking & Provenance
Intermediate · 45 min
As generated media floods the world, proving what is AI-made — and where real media came from — matters. Watermarking embeds invisible signals in output; provenance standards (like content credentials) attach a tamper-evident history to a file.
Skills: Invisible watermarking · Content credentials · Provenance metadata · Detection limits
Build it: Why is watermarking generated text much harder than watermarking generated images? What can an attacker do to each?
P6. Ship a Generative Product
Advanced · 60 min
The finish line: fold everything — generation, grounding, evals, guardrails, serving, and cost control — into a real product people can use safely and you can afford to run. A conversational assistant is the canonical example that ties the whole roadmap together.
Skills: End-to-end architecture · Safety + cost + quality · User experience · Launch checklist
Build it: Draw the architecture of a generative assistant: where do grounding, guardrails, caching, and evals each sit in the request path?