Context Engineering Guide - Revised Syllabus

Version: 2.0

Date: 2025-11-28

Status: Approved for Writing

Change Log

| Version | Date | Changes | |---------|------|---------| | 1.0 | 2025-11-28 | Initial syllabus from source materials | | 2.0 | 2025-11-28 | Refined after topic analysis - structure confirmed with minor adjustments |

Analysis Summary

| Chapter | Content Volume | Gaps Found | Overlaps | Recommendation | |---------|---------------|------------|----------|----------------| | Ch 1: Why Prompts Fail | High | None | Defines core concepts | ✅ Keep as is | | Ch 2: Good Context | High | Minor - more non-tech examples | Templates shared | ✅ Keep as is | | Ch 3: Before Prompt | High | None | Principles referenced later | ✅ Keep as is | | Ch 4: During/After | High | None | Context contamination | ✅ Keep as is | | Ch 5: Tools | Medium | Could add alternatives brief | Tool references | ✅ Keep as is | | Ch 6: Examples | Very High | None | Uses all concepts | ✅ Keep as is | | Ch 7: Teams | High | None | Uses templates | ✅ Keep as is | | Ch 8: Vibe Coding | High | Could expand MCP | Karpathy callback | ✅ Keep as is | | Ch 9: AI Won't Replace | Medium | None | Philosophy conclusion | ✅ Keep as is |

Conclusion: Original structure is solid. No merges, splits, or reorders needed.

Gap Resolution Plan

Critical Gaps (must fix)

None identified - all chapters have sufficient source material

Nice-to-Have (fix during writing if time)

| Gap | Chapter | Resolution | |-----|---------|------------| | More non-technical examples | Ch 2, Ch 3 | Add garden/car seat as additional examples | | MCP explanation | Ch 8 | Brief mention, don't deep dive | | Tool alternatives | Ch 5 | Brief mention of why not used (Cursor, Copilot) |

Gaps to Ignore

| Gap | Reason | |-----|--------| | Deep MCP tutorial | Out of scope - technical rabbit hole | | Tool pricing details | Prices change - keep ranges only | | Job market statistics | Keep philosophical, not economic |

Overlap Resolution

| Content | Primary Location | Secondary Use | |---------|------------------|---------------| | AI as colleague metaphor | Ch 1.3 | Reference only in Ch 4, Ch 9 | | Prompt vs Context definition | Ch 1.2 | Reference in Ch 2 | | Task template | Ch 2.2 | Ch 6 (examples), Ch 7 (team version) | | Karpathy quote | Ch 1.4 | Callback in Ch 8 | | Context contamination | Ch 4.3 | Mention in Ch 2, Ch 8 | | 2-minute rule | Ch 4.1 | First mention in Ch 1 | | Junior developer test | Ch 2.4 | Reference in Ch 6 | | Car seat example | Ch 1 (intro) | Ch 3 (iteration), Ch 6 (if needed) |

Rule Applied: Concepts defined once, referenced thereafter. No full re-explanations.

Narrative Flow Confirmed

Reader Journey:

Part 1: Understanding (WHY)
  Ch 1: Problem → Why prompts fail
  Ch 2: Solution → What good context looks like

Part 2: Practice (HOW)
  Ch 3: Before → Preparation steps
  Ch 4: During/After → Iteration techniques
  Ch 5: Tools → What to use for what

Part 3: Application (WHAT)
  Ch 6: Examples → See it in action
  Ch 7: Teams → Scale it up

Part 4: Bigger Picture (SO WHAT)
  Ch 8: Industry → Vibe coding critique
  Ch 9: Future → AI won't replace you

Flow Check:

✅ After Ch 1, reader has foundation for Ch 2
✅ Logical progression: problem → solution → practice → application → philosophy
✅ Practical chapters (6-7) come after conceptual (1-5)
✅ Ending satisfying ("overcome yourself" philosophy)

Final Structure

Part 1: Understanding Context Engineering

Chapter 1: Why Your AI Prompts Fail

1.1 The "Giving Little, Expecting Much" Trap

Source: Article 1, Interview Q20
Key: Personal evolution story, most people's mistake

1.2 Prompt vs Context - The Real Difference

Source: Article 1, Interview Q19
Key: Prompt = task, Context = everything else

1.3 AI as Your New Colleague

Source: Article 1, Interview Q8-9
Key: Knows nothing but can learn everything, 24/7, trade-offs

1.4 Industry Validation

Source: Article 9
Key: Karpathy quote, "context engineering" term

Chapter 2: What Good Context Looks Like

2.1 The Five Components of Context

Source: Article 2, Presentation
Key: Task, Constraints, Background, Examples, Success Criteria

2.2 The Universal Task Template

Source: Article 2, Article 7
Key: Problem, Context, Goal, Solutions, Tests

2.3 Before and After: Real Examples

Source: Article 1, Article 3
Key: Expense tracker, article writing examples

2.4 The Junior Developer Test

Source: Article 3, Interview Q24
Key: "Could a new colleague complete this?"

Part 2: The Practice

Chapter 3: Before You Prompt - Preparation

3.1 Give the WHY, Not Just WHAT

Source: Presentation, Interview Q1-3
Key: AI doesn't know priorities, car seat example

3.2 Break Into Atomic Parts

Source: Article 4, Interview Q5
Key: Smaller = better, atomization principle

3.3 One Example Beats 1000 Words

Source: Article 2, Article 3
Key: Show don't tell, attach previous work

3.4 Say What to EXCLUDE

Source: Article 8
Key: AI includes everything unless told not to

3.5 Define What DONE Looks Like

Source: Article 3
Key: Clear success criteria, verifiable outcomes

Chapter 4: During and After - Iteration

4.1 The 2-Minute Rule

Source: Article 1, Article 4
Key: Signal not failure, when to stop and adjust

4.2 Explain WHY It's Wrong

Source: Article 3, Interview Q7
Key: AI learns from "wrong because X"

4.3 Context Contamination

Source: Article 2, Article 3, Article 9
Key: Bad output stays in context, when to start fresh

4.4 Ask for Opinion, Not Validation

Source: Article 3, Interview Q18
Key: Models tuned to praise, better questions

4.5 Editing Prompts Mid-Work

Source: Article 2, Interview Q11
Key: Zed approach, don't send correction messages

Chapter 5: Tools and Workflows

5.1 Choosing the Right Tool

Source: Article 6, Interview Q12-13
Key: Perplexity/Claude/ChatGPT division by task type

5.2 Research Workflow

Source: Article 2, Article 6
Key: Perplexity → extract → insert into next tool

5.3 Coding Workflow

Source: Article 6, Interview Q3-4
Key: Zed + Claude, context management

5.4 Tool Combinations

Source: Article 6
Key: What works together, what doesn't

Part 3: Real-World Application

Chapter 6: Practical Examples

6.1 Debugging Production Bug

Source: Article 8
Key: Full bad/good comparison

6.2 Technology Selection

Source: Article 8
Key: Framework for admin panel

6.3 SQL Optimization

Source: Article 8
Key: From 8s to 0.3s

6.4 Documentation Generation

Source: Article 8
Key: OpenAPI spec, style matching

6.5 Legacy Code Refactoring

Source: Article 8
Key: Step-by-step, don't change constraints

6.6 Personal Projects

Source: Article 5, Article 8, Interview Q15
Key: Garden, solar panel analysis

Chapter 7: Context Engineering for Teams

7.1 Why Teams Fail with AI

Source: Article 7
Key: Licenses without process, 2-hour training myth

7.2 Documentation as Context

Source: Article 7
Key: "Everyone knows" doesn't work

7.3 Implementation Roadmap

Source: Article 7
Key: 6-week plan

7.4 Roles and Responsibilities

Source: Article 7
Key: PO, Developer, QA, PM roles

7.5 Measuring Success

Source: Article 7
Key: What to track, what NOT to track

7.6 Cultural Change

Source: Article 7
Key: Mindset shifts, how to achieve

Part 4: The Bigger Picture

Chapter 8: Vibe Coding vs Context Engineering

8.1 The Vibe Coding Reality

Source: Article 9
Key: Horror stories, Borovička analogy

8.2 When Vibe Coding Works

Source: Article 9
Key: Prototypes, scripts, experiments

8.3 The Parallel Agents Trap

Source: Article 4, Article 9
Key: Dependent tasks can't parallelize

8.4 QA as Bottleneck

Source: Article 7, Article 9
Key: Fast generation, slow verification

Chapter 9: AI Won't Replace You

9.1 The Imposter Syndrome

Source: Article 5, Interview Q22
Key: Right approach, do more

9.2 What AI Still Can't Do

Source: Article 5, Interview Q16
Key: Situational awareness, traffic example

9.3 AI as Amplifier

Source: Article 5
Key: Garden and electricity wow moments

9.4 AI in Education

Source: Article 5, Interview Q24
Key: Custom GPT mentor idea

Appendix

A: Quick Reference - The 10 Advices

Source: Presentation
Format: One-page summary checklist

B: Templates

Task template
Bug report template
Feature request template

C: Tool Comparison

Perplexity vs Claude vs ChatGPT
When to use which

D: Citations and Resources

Karpathy quotes
Chroma Research
External links

Writing Order

Recommended order for chapter writing:

Chapter 1 - Foundation, sets tone for everything
Chapter 2 - Core concept, most referenced by later chapters
Chapter 3-4 - Practice, build on 1-2
Chapter 5 - Tools, practical bridge
Chapter 6 - Examples, ties all concepts together
Chapter 7 - Teams, scaling
Chapter 8-9 - Big picture, conclusion
Appendix - Extract from written chapters

Rationale: Concepts must be defined before they can be referenced or applied.

Dependencies

| Chapter | Depends On | Must Complete Before | |---------|------------|---------------------| | Ch 1 | None | All others | | Ch 2 | Ch 1 | Ch 3, Ch 6, Ch 7 | | Ch 3 | Ch 1, Ch 2 | Ch 4, Ch 6 | | Ch 4 | Ch 1, Ch 2, Ch 3 | Ch 6, Ch 8 | | Ch 5 | Ch 1, Ch 2 | Ch 6 | | Ch 6 | Ch 1-5 | Ch 7 | | Ch 7 | Ch 2, Ch 6 | Ch 8 | | Ch 8 | Ch 4 | Ch 9 | | Ch 9 | Ch 1, Ch 8 | None | | Appendix | All chapters | None |

Estimated Final Length

| Section | Est. Words | Est. Pages | |---------|------------|------------| | Introduction | 800 | 2 | | Part 1 (Ch 1-2) | 5,000 | 10 | | Part 2 (Ch 3-5) | 6,500 | 13 | | Part 3 (Ch 6-7) | 5,500 | 11 | | Part 4 (Ch 8-9) | 4,000 | 8 | | Appendix | 2,000 | 4 | | Total | ~24,000 | ~48 pages |

Quality Criteria Check

| Criterion | Status | |-----------|--------| | All gaps have resolution plan | ✅ | | No chapter too short (<2,000 words expected) | ✅ | | No chapter too long (>6,000 words expected) | ✅ | | Overlaps resolved (one primary location each) | ✅ | | Flow makes logical sense | ✅ | | All changes documented | ✅ | | Revised syllabus is complete | ✅ |

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