Phase 2: The Engineering of Reasoning

Canonical Source of Trust: Google Doc Tab Phase 2
Training Program: [Huy Chau] Generative AI Training Plan

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Advanced Reasoning Techniques

1. What to Learn

• Logical Decomposition:
  • Chain-of-Thought (CoT): Guiding models to solve problems via step-by-step reasoning, explicitly showing intermediate steps.
  • Chain of Draft (CoD): A standard technique forcing the model to produce concise, sketch-like reasoning before the final answer to reduce token consumption while maintaining logical rigor.
• Strategic Exploration:
  • Tree of Thoughts (ToT): Exploring multiple solution paths at each step, branching out like a decision tree to evaluate viability and backtrack when necessary.
  • Self-Consistency: Generating multiple independent reasoning chains and using a majority vote or LLM-as-a-judge to select the most reliable result.
• Abstraction Techniques:
  • Step-Back & Meta Prompting: Requesting a broad analysis or outline of principles and refined prompt generation before narrowing down to a specific answer.
  • Analogical Prompting: Instructing the model to generate its own relevant examples/analogies to prime its weights with context before tackling the actual problem.
• Interactive Reasoning:
  • ReAct (Reason + Act): Alternating between "Thoughts" and "Actions" (tool calls) with "Observations" in between to solve tasks involving external data or APIs.
• Trajectory Analysis:
  • Reading Multi-Step Reasoning Traces
  • Identifying Reasoning Deviations
  • Incorrect Tool Selection
  • Doom Loop Entry Points
• Compulsory Security:
  • **Alignment Auditing: Learning to implement AlignmentCheck, a semantic-layer defense, to **monitor the reasoning trace to detect “Goal Hijacking” if the model’s logic shifts away from the original user objective.

2. How to Learn

• Hill-Climbing Logic: Iteratively refine reasoning prompts to fix "doom loops", where models repeat broken logic; practice using loop detection to trigger a step-back.
• Self-Consistency Drills: Generating multiple paths and using LLM-as-a-judge to pick the best reasoning chain.
• Comparative Benchmark Testing: Evaluate how different reasoning techniques (e.g., CoT vs. ToT) perform on the HumanEval or Terminal Bench 2.0 datasets.
• Trace Analysis: Use LangFuse to visualize the execution tree of a multi-step reasoning prompt to identify exactly where a model "loses the plot" or enters a loop

3. Where to Learn

• **E-book: **AI Agents and Applications (Chapter 2, Section 2.5 on "Reasoning in Detail")
• Advanced Guides: Advanced Prompt Engineering Techniques (Patronus AI).
• Reference:
  • LlamaFirewall: An open source guardrail system for insights on AlignmentCheck and reasoning traces

4. Subjective Outputs Required

• The Multi-Path Planner: A design for a software feature implementation using Tree of Thoughts, including a brainstorm of three approaches, evaluations of each, and a final chosen path.
• The Efficiency Report: A documented comparison of a logic problem solved via Chain-of-Thought versus Chain of Draft, measuring token usage and reasoning accuracy for both.
• The Self-Correcting Research Assistant: A prompt-based agent using the ReAct framework that can search for information, observe results, and refine its next steps autonomously.
• The Meta-Refiner: A "Meta-Prompting" system where the developer uses one LLM to generate or refine prompts for another to handle underspecified user queries.
• Alignment Audit Proof: A verifiable trace from a debugger showing an agent detecting its own goal drift and successfully halting execution before performing an unauthorized action