Appendix
Quick reference materials for daily use.
A: The 10 Key Advices
Before You Prompt (5 Advices)
| # | Advice | What It Means | |---|--------|---------------| | 1 | Give the WHY, not just WHAT | Explain your priorities and reasoning—AI can then optimize for what actually matters to you | | 2 | Break into atomic parts | Smaller tasks = dramatically better results. If you can't explain it in 1 minute, it's too big | | 3 | One example beats 1000 words | Show AI what you want: previous work, style references, format samples | | 4 | Say what to EXCLUDE | AI includes everything unless told otherwise. Be explicit about what you don't want | | 5 | Define what DONE looks like | Clear success criteria: "Query under 500ms" not "faster query" |
During and After (5 Advices)
| # | Advice | What It Means | |---|--------|---------------| | 6 | 2-minute rule | If AI takes longer than 1-2 minutes, something's wrong. Stop and adjust | | 7 | Explain WHY it's wrong | "Wrong because X" teaches AI; "that's wrong" doesn't | | 8 | Watch for context contamination | Bad output pollutes future responses. Sometimes starting fresh is faster | | 9 | Ask for opinion, not validation | "What would you change?" beats "Is this good?" | | 10 | Edit prompts, don't send corrections | Update original request instead of adding "sorry, I meant..." |
Quick Test Before Every Task
"Could a junior developer who started yesterday complete this task with just this information?"
If no → add more context. If yes → AI can handle it.
B: Templates
Universal Task Template
## Problem
[What broke / what needs to be done]
## Context
- Files: [specific files and lines if applicable]
- History: [relevant previous changes or attempts]
- Constraints: [what must not change]
## Goal
[Clear success criterion—when is it done]
## Possible Solutions
1. [First option to explore]
2. [Second option]
3. [Third option]
## Tests/Verification
[How we verify it works]
Bug Report Template
## Bug Description
[One sentence: what's wrong]
## Steps to Reproduce
1. [First step]
2. [Second step]
3. [Step where bug occurs]
## Expected Behavior
[What should happen]
## Actual Behavior
[What actually happens]
## Environment
- File: [specific file and line if known]
- Browser/OS: [if relevant]
- Error message: [exact text]
## What I've Tried
- [First attempt and result]
- [Second attempt and result]
## Success Criteria
[How we know it's fixed]
Feature Request Template
## Feature Summary
[One sentence description]
## User Story
As a [role], I want [capability], so that [benefit].
## Context
- Why now: [business reason]
- Who requested: [source]
- Priority: [high/medium/low]
## Requirements
- Must have: [essential features]
- Nice to have: [optional features]
- Out of scope: [explicitly excluded]
## Technical Context
- Affected files: [if known]
- Related features: [existing functionality]
- Constraints: [backwards compatibility, performance, etc.]
## Acceptance Criteria
- [ ] [First criterion]
- [ ] [Second criterion]
- [ ] [Third criterion]
## Test Scenarios
- Happy path: [expected usage]
- Edge case: [unusual but valid usage]
- Error case: [what should fail gracefully]
Code Refactoring Template
## Target Code
[File path and function/class name]
## Current State
- Lines: [count]
- Problem: [why it needs refactoring]
- Tests: [existing test coverage]
## Refactoring Goals
1. [First goal - be specific]
2. [Second goal]
3. [Third goal]
## DO NOT Change
- [First constraint]
- [Second constraint]
- [Business logic / API / etc.]
## Process
1. Analyze and propose structure (don't code yet)
2. Wait for approval
3. Implement with tests
4. Verify all original tests pass
C: Tool Comparison
When to Use Which Tool
| Task Type | Best Tool | Why | |-----------|-----------|-----| | Research & Information | Perplexity | Quick summaries with sources, easy to extract key parts | | Programming & Technical | Claude (via Zed or API) | Better code understanding, context management, longer sessions | | Writing & Non-Technical | ChatGPT | Conversational, great for brainstorming, home/life tasks | | Complex Analysis | Claude | Handles nuance, technical depth, longer context | | Quick Q&A | Any | For simple questions, any tool works |
Tool Combinations That Work
| Combination | Use Case | |-------------|----------| | Perplexity → ChatGPT | Research topic → Write article/summary | | Perplexity → Claude | Find API docs → Implement feature | | Claude → ChatGPT | Generate code → Explain to non-technical stakeholder |
What NOT to Combine
| Combination | Why It Doesn't Work | |-------------|---------------------| | ChatGPT + Claude for same task | Different strengths, creates confusion | | Multiple tools in parallel on same problem | Context diverges, results conflict |
Tool Limitations
Perplexity:
- Often lacks latest data (verify dates)
- Sometimes "invents" sources (click through to verify)
- Superficial on deep technical topics
Claude:
- Struggles with similar file names in different directories
- Performance drops with more than 3-4 files in context
- May not know latest library versions
ChatGPT:
- Less precise for code generation
- Can be verbose
- May not maintain consistency in long sessions
D: Citations and Resources
Key Quotes
Andrej Karpathy (OpenAI Co-founder, Former Tesla AI Director)
"+1 for 'context engineering' over 'prompt engineering'. People associate prompts with short task descriptions. In every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step."
— Twitter/X, 2025
George Arrowsmith (QA Perspective)
"QA is about to become a huge bottleneck in software development. AI lets us churn out HUGE amounts of code extremely fast, but you still need to make sure it works."
— LinkedIn, 2025
Milan Martiniak (Developer Time Allocation)
"Programmers spend less than 20% of their work time actually programming."
— Survey, 2025
Key Concepts Defined
| Term | Definition | |------|------------| | Context Engineering | The practice of providing AI with just the right information for the task—no more, no less | | Prompt | The specific task or question you give AI | | Context | Everything else: background, constraints, examples, success criteria | | Vibe Coding | AI-assisted coding with minimal understanding or verification | | Context Contamination | When bad AI output remains in conversation and influences future responses | | Atomization | Breaking large tasks into small, focused pieces |
The Five Components of Good Context
- Task — What you want AI to do
- Constraints — What AI must NOT do
- Background — Why you need this
- Examples — What good output looks like
- Success Criteria — How you'll judge the result
E: Decision Trees
Fix or Start Fresh?
Is output at least 80% correct?
├─ YES → Fix it
│ └─ Does AI keep making same mistake after correction?
│ ├─ YES → Start fresh
│ └─ NO → Continue fixing
└─ NO → Start fresh
└─ When starting fresh:
├─ Include what you learned from failed attempt
├─ Be more specific about constraints
└─ Add examples of what you want
Should I Use AI for This Task?
Is the task well-defined?
├─ NO → Define it first, then reconsider
└─ YES → Continue
Do I have the context needed?
├─ NO → Gather context first
└─ YES → Continue
Is it a one-time task or repeated?
├─ ONE-TIME → Use AI directly
└─ REPEATED → Create template/automation
What's the cost of errors?
├─ HIGH (production, customer-facing) → Use AI with careful review
├─ MEDIUM (internal, non-critical) → Use AI with normal review
└─ LOW (prototype, experiment) → Vibe code away
F: Quick Start Checklist
For Your First Week with Context Engineering
- [ ] Pick one tool to start (Perplexity, Claude, or ChatGPT)
- [ ] Use the Universal Task Template for 5 different tasks
- [ ] Apply the 10 Key Advices consciously
- [ ] Notice when AI fails—what context was missing?
- [ ] Try the "junior developer test" before each prompt
For Your First Month
- [ ] Develop your personal task templates
- [ ] Identify which tool works best for which task type
- [ ] Practice the fix-or-restart decision
- [ ] Share what works with a colleague
- [ ] Track one metric: tasks completed first-try vs. needed iteration
For Team Implementation
- [ ] Start with 2-3 person pilot, non-critical project
- [ ] Document what works and what doesn't
- [ ] Create team task templates
- [ ] Define review process for AI output
- [ ] Set expectations: cultural change takes time
G: Common Mistakes Quick Reference
| Mistake | Fix | |---------|-----| | Task too vague | Add specific files, constraints, success criteria | | Too much context | Focus on relevant information only | | No examples | Attach previous work or style references | | Missing constraints | Explicitly state what NOT to change | | Asking for validation | Ask for criticism instead | | Sending corrections | Edit original prompt | | Waiting too long | Apply 2-minute rule—stop and adjust | | Fighting bad context | Start fresh with improved prompt | | No success criteria | Define verifiable "done" state | | Parallel dependent tasks | Run sequentially, parallel only independent work |
End of Guide