Reasoning & Chain-of-Thought
Guiding models to reliable multi-step answers.
Module Overview
Methods to coax stepwise reasoning from LLMs, compare direct answers vs chain-of-thought approaches, and combine models with external deterministic checkers. Focus on reliability and verification.
Learning Objectives
- Construct prompts that elicit stepwise reasoning and decomposition for multi-step tasks.
- Evaluate when chain-of-thought improves correctness and when it increases hallucination risk.
- Design verification steps (self-check prompts, cross-checks, external calculators, regex validators).
Lesson-by-Lesson Breakdown
Decomposition strategies: task splitting and subtask framing.
Chain-of-thought prompting: examples, pitfalls, and self-consistency techniques.
Verification patterns: assert-check, secondary-model-check, and deterministic validators.
Hybrid pipelines: LLM + calculator / LLM + regex / LLM + reference data.
Error analysis: categorize errors and remediation strategies.
Reporting & transparency: how to show parents what was checked and why.
Hands-on Activities & Deliverables
Activities
Build prompts that solve multi-step arithmetic/logic problems with verifiable steps; produce a comparison report (accuracy & failure cases) with visualizations of error types.
📦 Deliverable
Notebook or script showing inputs, outputs, verification steps, and final correctness metrics.
Required Tools & Readings
Datasets of reasoning tasks, simple test harness, articles on chain-of-thought prompting.
Assessment & Rubric
- Correctness after verification50%
- Quality and clarity of stepwise outputs25%
- Thoroughness of error analysis25%
Prerequisites
Modules 1–2 recommended.
Parent-Friendly Value
Ensures student-produced answers are transparent and verifiable — helpful for homework checking and tutoring scenarios.