Midjourney Style Pack: 180 Recipes for Product and UI Art
by Laura Mbeki
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Programming, AI and prompt-engineering courses written by working engineers. Filter by language, level, price or rating — buy once, get an instant email access link, keep it for life.
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Showing 49–72 of 90 courses
One hundred and eighty tested Midjourney recipes for hero art, icon sets and mockups.
Ninety debugging prompts that push a model past the obvious answer toward the real cause.
One hundred and twenty prompts that turn untested code into a suite you can trust.
Nine end-to-end workflows taking a ticket from vague request to reviewed pull request.
Twenty-four agent blueprints with tool schemas, system prompts and real stop conditions.
Prompts that turn commits, diffs and tickets into documentation people will actually read.
Prompts that write, explain and stress-test SQL against a schema you paste in yourself.
Understand why models drift, then write instructions that hold across a long conversation.
Get valid JSON on the first attempt, and handle the cases where you still will not.
Fit the right material into a context window and cut token spend without losing accuracy.
Design tool schemas a model calls correctly, and handle the failures when it does not.
Build a small eval suite that tells you honestly whether a prompt change was an improvement.
Move from clever one-off prompts to versioned, tested prompt systems a team can maintain.
Ship model features that refuse the right things and never leak your system instructions.
Expose your own data and actions to assistants through MCP servers you write and control.
The full craft: prompt architecture, agent loops, evaluation harnesses and cost discipline.
Your first month with model APIs: keys, streaming, retries, cost and one shipped feature.
Understand embeddings well enough to build search that finds meaning, not just keywords.
Chunking, hybrid search and reranking, the parts that decide whether retrieval really works.
Pick, index and operate a vector store without turning your search into a science project.
Make assistants a genuine part of your workflow, not a party trick abandoned by Friday.
Build a dataset, run a LoRA fine-tune, and prove it beats prompting for your narrow task.
The maths-light foundation that makes model behaviour, metrics and failures make sense.
Queues, caching, streaming, fallbacks and budgets: the architecture behind a reliable AI feature.