Lesson 1183 of 2116
Chain-of-Thought for Production: When It Helps, When It Hurts, Part 1
Complex workflows need decision logic. Prompt decision trees encode logic that adapts to inputs.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Building Staged Prompt Pipelines vs One Mega-Prompt
- 3The premise
- 4AI Prompting: Know When to Reach for a Reasoning Model
Concept cluster
Terms to connect while reading
Section 1
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
What AI cannot do
- Anticipate every input edge case
- Substitute decision trees for actual logic
- Make complex workflows simple
Key terms in this lesson
Section 2
Building Staged Prompt Pipelines vs One Mega-Prompt
Section 3
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
What AI cannot do
- Eliminate end-to-end variance
- Reduce cost in every case (more calls = more tokens)
- Replace good prompt writing per stage
Section 4
AI Prompting: Know When to Reach for a Reasoning Model
Section 5
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
What AI cannot do
- Predict reasoning quality on novel tasks
- Account for your provider's pricing changes
- Replace measuring real task outcomes
Section 6
AI Prompting: Decompose Hard Tasks Into Reliable Sub-Prompts
Section 7
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
What AI cannot do
- Decide your latency budget
- Make compounding errors disappear — only contain them
- Replace integration testing of the chain
Section 8
AI and chain-of-thought vs direct answer
Section 9
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.
What AI cannot do
- Guarantee accuracy gain on your task.
- Hide reasoning from a determined inspector.
- Replace evals to confirm tradeoff.
Section 10
Chain-of-Density: Iterating Toward a Tighter Summary
Section 11
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.
What AI cannot do
- Know which facts are most important to your reader.
- Compress without sometimes dropping nuance.
Section 12
Decomposing a Hard Problem Into a Prompt Chain
Section 13
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.
What AI cannot do
- Know how to split your problem without your design.
- Recover quality lost to a bad stage boundary.
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “Chain-of-Thought for Production: When It Helps, When It Hurts, Part 1”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 40 min
Chain-of-Thought for Production: When It Helps, When It Hurts, Part 2
Use a reasoning step that you discard before showing the final answer.
Creators · 40 min
Few-Shot Example Curation: Quality, Rotation, and Counter-Examples, Part 1
Chain-of-thought prompts show real performance gains on reasoning tasks — and zero benefit on tasks that don't need reasoning. Here's how to tell which is which.
Builders · 40 min
Chain-of-Thought for Builders: Make AI Show Its Reasoning
Force AI to explain its reasoning out loud, and you'll catch its mistakes faster.