Prompt Engineering · TRACK 05/05
Prompt Iteration & Management
Treating prompts like code: test, version, optimize.
// THE TRACK
01 · START HEREPrompt ManagementUnderstand why serious teams version, review, and test prompts like code, and the basic workflow to start doing it yourself.BEGINNERPrompt TemplatesSee how production apps turn one-off prompts into parameterized templates, and learn the pitfalls of naive string interpolation.INTERMEDIATECommon Prompting MistakesDiagnose the ten failure patterns behind most bad LLM output and learn the one-line fix for each, so you stop blaming the model.BEGINNERA/B Testing PromptsLearn to compare two prompt versions with a real eval set and honest metrics instead of eyeballing three outputs and shipping a regression.INTERMEDIATEPrompt PlaygroundsTour the three major playgrounds, learn which knobs matter, and pick up a fast iterate-in-playground, ship-to-code workflow.BEGINNERMeta-PromptingLearn to turn the model into your prompt-writing partner — drafting, critiquing, and tightening prompts — and recognize where meta-prompting helps and where it misleads.BEGINNERReducing HallucinationsLearn the prompt-side tactics that measurably cut made-up answers — grounding, permission to abstain, citation demands — and why prompting alone can't fully fix it.INTERMEDIATEAutomatic Prompt OptimizationUnderstand how automatic prompt optimizers search for better wording and examples against a metric, what DSPy and OPRO actually do, and when it beats hand-tuning.ADVANCEDTextGradYou will understand how TextGrad borrows the backpropagation idea to optimize prompts, using LLM-written natural-language feedback as gradients that push each component to improve.ADVANCEDPromptLayerYou will understand how PromptLayer acts as a version-controlled registry for prompts, adding evals, A/B testing, and logging so prompt changes are tracked like code.INTERMEDIATE