The premise Complex workflows require decision logic; prompt decision trees adapt response to inputs.
What AI does well here Design decision trees with clear branch criteria Test branches with representative inputs Maintain logic clarity over time Integrate with broader workflow tools Prompt decision tree design Design prompt decision tree. Cover: (1) branch criteria, (2) representative input testing, (3) logic maintenance, (4) workflow integration, (5) failure handling, (6) measurement of decision quality. What AI cannot do Anticipate every input edge case Substitute decision trees for actual logic Make complex workflows simple Key terms: decision trees · workflows · logicPractitioner tip Treat every prompt as a spec: role → context → task → format. Review your first output as a draft, not a final. The second iteration is almost always better. Lesson complete You've completed "Prompt Decision Trees for Complex Workflows". Mark this lesson done and keep going — every lesson builds on the last. Building Staged Prompt Pipelines vs One Mega-Prompt The premise When a single prompt juggles too many goals, split it into stages with typed inputs/outputs and evaluate per stage.
What AI does well here Isolate failures to a stage Swap models per stage by need Cache stable early stages Stage contract template Stage N: input schema | output schema | success metric | model. Each stage independently testable. Pipeline runner enforces schemas between stages. What AI cannot do Eliminate end-to-end variance Reduce cost in every case (more calls = more tokens) Replace good prompt writing per stage Latency adds up Three sequential calls is three times the latency. Parallelize independent stages or accept the cost. Practitioner tip Treat every prompt as a spec: role → context → task → format. Review your first output as a draft, not a final. The second iteration is almost always better. Lesson complete You've completed "Building Staged Prompt Pipelines vs One Mega-Prompt". Mark this lesson done and keep going — every lesson builds on the last. AI Prompting: Know When to Reach for a Reasoning Model The premise Reasoning models excel at multi-step math, code synthesis, and ambiguous planning; they are wasteful and slower for retrieval, summarization, and well-specified transforms.
What AI does well here Match task class to model class Estimate the latency and cost delta Suggest a fast-path/slow-path router Recommend evals to confirm the upgrade is worth it Prompt: route this task List your tasks with sample inputs and expected outputs. Ask: 'For each, recommend reasoning or direct model and justify. Propose a router rule that picks at runtime.' What AI cannot do Predict reasoning quality on novel tasks Account for your provider's pricing changes Replace measuring real task outcomes Reasoning models can over-think simple tasks On well-specified transforms, reasoning models sometimes second-guess and produce worse output than a direct model. Always A/B before defaulting to the bigger hammer. Practitioner tip Treat every prompt as a spec: role → context → task → format. Review your first output as a draft, not a final. The second iteration is almost always better. Lesson complete You've completed "AI Prompting: Know When to Reach for a Reasoning Model". Mark this lesson done and keep going — every lesson builds on the last. AI Prompting: Decompose Hard Tasks Into Reliable Sub-Prompts The premise Mega-prompts hide failure inside one opaque call; decomposed chains let you measure each step, replace any one, and reason about overall reliability.
What AI does well here Identify natural step boundaries (extract → reason → format) Define inputs and outputs per step Place evals at each boundary Estimate end-to-end reliability from per-step rates Prompt: decompose this Paste your current mega-prompt. Ask: 'Decompose into 3-5 sub-prompts with clear inputs and outputs. Add an eval per boundary and compute compound reliability.' What AI cannot do Decide your latency budget Make compounding errors disappear — only contain them Replace integration testing of the chain Reliability compounds — downward Five 95%-reliable steps yield 77% end-to-end. Decomposition only helps if each step is genuinely better than the mega-prompt's slice of work. Practitioner tip Treat every prompt as a spec: role → context → task → format. Review your first output as a draft, not a final. The second iteration is almost always better. Lesson complete You've completed "AI Prompting: Decompose Hard Tasks Into Reliable Sub-Prompts". Mark this lesson done and keep going — every lesson builds on the last. AI and chain-of-thought vs direct answer The premise Reasoning aloud can boost accuracy but costs tokens and exposes intermediate logic. Choose deliberately, not by habit.
What AI does well here Suggest CoT for math, planning, multi-step logic. Suggest direct answers for classification and lookup. Propose hidden-CoT patterns for production. Prompt: CoT decision 'Task: <X>. Should I use chain-of-thought, hidden-CoT, or direct? Justify by accuracy, latency, cost, and whether reasoning leakage matters.' What AI cannot do Guarantee accuracy gain on your task. Hide reasoning from a determined inspector. Replace evals to confirm tradeoff. Watch out: CoT in low-latency UIs Visible CoT can double or triple response time. For chat UIs, stream a final answer or hide the trace. Practitioner tip Treat every prompt as a spec: role → context → task → format. Review your first output as a draft, not a final. The second iteration is almost always better. Lesson complete You've completed "AI and chain-of-thought vs direct answer". Mark this lesson done and keep going — every lesson builds on the last. Chain-of-Density: Iterating Toward a Tighter Summary The premise One-shot summaries are bloated. Asking the model to rewrite the same summary several times, each pass denser, produces tighter output.
What AI does well here Identify and remove filler across drafts. Add new entities while keeping length fixed. Density-pass prompt Ask: 'Write a 80-word summary. Then rewrite 3 times: each rewrite must keep length but add 2 missing entities and remove 2 filler phrases.' What AI cannot do Know which facts are most important to your reader. Compress without sometimes dropping nuance. Check the final version against the source Aggressive compression can introduce subtle inaccuracies. Diff the densest version against the source for facts that drifted. Practitioner tip Treat every prompt as a spec: role → context → task → format. Review your first output as a draft, not a final. The second iteration is almost always better. Lesson complete You've completed "Chain-of-Density: Iterating Toward a Tighter Summary". Mark this lesson done and keep going — every lesson builds on the last. Decomposing a Hard Problem Into a Prompt Chain The premise Asking the model to do five things at once degrades quality on all of them. Stage the work and route the output of one prompt into the next.
What AI does well here Do one well-scoped step at high quality. Use the previous stage's output as input. Stage chain template Define stages: extract -> classify -> draft -> critique -> finalize. Each stage gets its own prompt and only the inputs it needs. What AI cannot do Know how to split your problem without your design. Recover quality lost to a bad stage boundary. Errors compound across stages A 90% accurate stage chained five times is ~59% end-to-end. Add a check between stages or accept the compounding. Practitioner tip Treat every prompt as a spec: role → context → task → format. Review your first output as a draft, not a final. The second iteration is almost always better. Lesson complete You've completed "Decomposing a Hard Problem Into a Prompt Chain". Mark this lesson done and keep going — every lesson builds on the last. Key terms: decision trees · workflows · logic · pipeline · decomposition · single-shot · prompt architecture · reasoning model · task routing · fast path · extended thinking · prompt chain · step eval · compound reliability · chain of thought · reasoning · latency · cost · summarization · density · iteration · chain · stage