Chapter 1: AI Prompt Tutorial
AI Prompt Engineering (also called Prompting or Prompt Design).
Think of this as the skill that turns a good AI into a great one. In 2026, the models (Claude 4, GPT-5 family, Gemini 2.5, Grok-3/4, Llama 4, DeepSeek-R1, etc.) are extremely powerful — but they are still very literal interpreters of your instructions.
A mediocre prompt → mediocre / generic / wrong / hallucinated output A well-engineered prompt → output that feels like it was written by a domain expert with 10× less editing
Let’s learn it like a real teacher would: step-by-step, with patterns that actually work in 2026, many real before/after examples, and the current most effective mental models.
1. First — What really changed between 2023–2025 and 2026?
| Era | Main lever | Temperature still important? | Reasoning models dominant? | Best trick length |
|---|---|---|---|---|
| 2023–mid 2024 | Long, detailed prompts | Yes, very | Almost none | Very long |
| Late 2024–2025 | Chain-of-Thought + format | Medium | o1, Claude 3.5/4 thinking | Medium-long |
| 2026 | Reasoning effort + structure + constraints | Almost irrelevant on reasoning models | Yes — most strong models do hidden CoT | Short & surgical for many tasks |
2026 truth: For normal chat / creative / writing tasks → short + clear usually wins For hard reasoning / math / planning / analysis → force high reasoning effort + structure
2. The KERNEL framework (one of the most practical 2026 patterns)
Many power users converged on similar ideas. One popular version floating around teams in 2026 is KERNEL:
- K — Keep it simple (one clear goal)
- E — Easy to verify (success looks like X)
- R — Reproducible (no vague time words like “current”, “latest”)
- N — Narrow scope (one task per prompt if complex)
- E — Explicit constraints & format
- L — (some add) Language / tone / examples if style matters
Real example — bad vs good
Bad prompt (very common in 2025 still) “Help me write something about Redis for my blog”
Good 2026-style (KERNEL)
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Task: Write a 600–750 word technical blog post section Topic: Redis as a caching layer — explain why, when to use it, core concepts (key-value, eviction policies, TTL) Audience: Mid-level backend developers who know SQL but not much NoSQL Tone: Clear, professional, slightly friendly Include: - 1 short real-world use-case story - 2 code snippets (Python + redis-py) - 1 comparison table (Redis vs Memcached vs database caching) Format: Markdown with ## headings, code blocks, bullet lists Do NOT: mention Redis Cluster, Streams, or modules |
→ First try usually usable with very minor edits
3. The universal 2026 prompt template (copy-paste this structure)
Most strong results follow roughly this skeleton:
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# Identity / Role (optional but powerful) You are a senior [role] with 15 years experience in [field]. You are precise, skeptical and never make up facts. # Task (one clear sentence) [Do this one main thing] # Context / Input data • Fact 1: ... • Document / numbers / code: [paste here] • User profile / preferences: ... # Reasoning instructions (very important in 2026) Think step-by-step with high reasoning effort. Show your reasoning before the final answer. If uncertain — say "I don't know" or "probability ≈ 40%" instead of hallucinating. # Constraints & style rules • Length: 300–500 words / 4–7 bullet points / code < 80 lines • Tone: professional / friendly / sarcastic / ELI5 • Language: [en / hi / etc.], use [simple / technical] vocabulary • Never: apologize, say "as an AI", add disclaimers unless asked • Always: number steps, use tables for comparisons, use ``` for code # Output format (make it machine-parseable when possible) Use this exact structure: ## Title [short title] ## Summary One paragraph ## Details • Point 1 • Point 2 ## Code (if applicable) ```python |
References / Confidence
• Source 1 • My confidence: 90%
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### 4. Quick examples — before vs after (real 2026 improvements) **Task: Explain quantum entanglement like I'm 12** Before (typical 2024 prompt) "explain quantum entanglement to a 12 year old" After (2026 style) |
You are a children’s science communicator like Bill Nye. Explain quantum entanglement so a curious 12-year-old understands the core idea. Use only everyday analogies (no math). Maximum 180 words. Structure:
- Normal everyday example first
- What is different with particles
- Why it’s “spooky”
- One sentence: why scientists care End with one fun “wow” fact.
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→ Usually gets much clearer, more engaging explanations. **Task: Write clean Python code** Before "python function to find prime numbers" After |
Write a Python 3.11+ function.
Name: find_primes_up_to Argument: n (int) → returns list of all primes ≤ n Use Sieve of Eratosthenes (most efficient for this task) Include:
- Type hints
- Docstring (Google style)
- Basic validation (raise ValueError if n < 0)
- One example usage in if name == “main“
Code only — no explanations outside the docstring.
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### 5. Top techniques still very strong in 2026 | Technique | When to use | 2026 strength (1–10) | Quick example phrase | |-----------------------|------------------------------------------|-----------------------|--------------------------------------------------| | Few-shot | Format, style, tone must match exactly | 9 | "Here are 3 examples: [input → output] ×3" | | Chain-of-Thought | Math, logic, planning, debugging | 10 (especially reasoning models) | "Think step by step. Show every reasoning step." | | Role / Persona | Tone, depth, bias control | 8–9 | "You are a cynical but honest CTO reviewing code"| | Output format lock | API, parsing, consistency | 10 | "Respond ONLY with JSON: {…}" | | Constraints first | Prevent hallucinations / scope creep | 9 | "Use only facts present in <context>. If unsure — write 'UNCERTAIN'" | | Self-ask / decompose | Very complex questions | 8 | "First break this into 3–5 sub-questions…" | | High reasoning effort | Hard analytical tasks (on models that support it) | 10 on o1-style models | "Use maximum reasoning effort. Think deeply." | ### Quick checklist before you hit send (2026 power-user habit) 1. Is the **task** one sentence and crystal clear? 2. Did I say **exactly** what success looks like (length, format, must-have pieces)? 3. Did I forbid dangerous things (hallucinate, apologize, be vague)? 4. Did I give 1–3 examples if format/style is important? 5. For reasoning — did I say "think step-by-step" or "high reasoning effort"? 6. For code/data → did I say "only use packages X, Y"? Got it? 🔥 Now tell me what you want to practice: - Write better image generation prompts (Midjourney / Flux / Imagen)? - Prompt for code / debugging? - Research / analysis / long reports? - Creative writing / storytelling? - Make a prompt for your specific use-case (tell me what you do)? Your turn — let's engineer something together! 🚀 |
