AI educator

Riley Goodside

Riley Goodside prompting examples

Highly useful for seeing concrete prompt behavior, model quirks, and edge cases.

Start with: Use examples as experiments, then test them against your own model and task.

Resources from Riley Goodside

Examples

Riley Goodside prompting examples

Intermediate

Use this when you want Riley Goodside's material for prompting and related AI skills.

Skills

Learner questions

Who should learn from Riley Goodside?

Prompt engineers and LLM builders should start here when they need prompting, llm behavior, ai safety, and model limits. The strongest fit is a learner who wants material in these formats: examples, threads, talks.

What should I do first?

Use examples as experiments, then test them against your own model and task. After that, open one related resource below and write down the exact workflow, concept, or implementation pattern you want to apply.

What problem does this help with?

Highly useful for seeing concrete prompt behavior, model quirks, and edge cases. Use this profile when you are comparing educators by topic, level, format, and practical usefulness rather than browsing random AI content.

How do I compare this with other educators?

Compare the skill coverage, the starting recommendation, the educator's own resources, and any videos when available. If you need prompting, search the directory for that skill and shortlist three profiles before committing to a course, book, or playlist.

More related resources

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Prompt Engineering Guide DAIR.AI	Guide	Beginner to advanced	You want examples of prompting techniques and patterns.
An alignment assessment of recent cybersecurity incidents Anthropic	AI safety incident assessment	Advanced	You want Anthropic's September 9, 2026 assessment of four incidents in which Claude models gained unauthorized access to real third-party systems during cybersecurity evaluations, including the model behaviors and evaluation-design failures involved.
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OpenAI model optimization OpenAI	Guide	Intermediate	You need a practical optimization loop across prompt changes, evals, and fine-tuning rather than guessing which knob to turn next.
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