Follow the Prompt Engineering & Context Design learning path on AI Builders Network - a free, structured roadmap with curated resources and step-by-step progress tracking.
This path is for anyone who uses LLMs in real work, builders, analysts, writers, support leads, and wants outputs they can rely on instead of re-rolling until something looks right. It treats prompts as production artifacts: designed, tested, and versioned.
By the end you'll be able to: Write structured prompts that collapse output variance; Get guaranteed-parseable JSON with structured output modes; Design context windows: retrieval, history, and tool results; Defend prompts against injection using OWASP guidance; Run regression tests on prompts with an eval harness.
Yes, but it matured. Clever one-line tricks died; what companies pay for now is context engineering, designing everything that enters the model's context window, plus systematic evaluation and versioning. The discipline looks less like wordsmithing and more like API design, and it remains a hiring filter for AI roles.
Prompt engineering writes the instructions; context engineering decides everything else the model sees: retrieved documents, conversation history, tool results, and their ordering. In production systems the context usually dwarfs the prompt, which is why context design now matters more than instruction phrasing alone.
Build a small evaluation set: 20-50 real inputs with what 'good' looks like for each, then run every prompt change against it with a harness like promptfoo. If you cannot measure a prompt change, you cannot tell improvement from luck: eyeballing three outputs is not testing.