Prompt Engineering Knowledge Cards

The Google Prompt Engineering Whitepaper is excellent, so I created a set of knowledge cards with ChatGPT, 😄.

🛠️ Best Practices for Effective Prompting

Principle	Key Idea	Example / Tip
Provide Examples	Use one-shot or few-shot examples to show the model what good output looks like.	✅ Include 3-5 varied examples in classification prompts.
Design with Simplicity	Clear, concise, and structured prompts work better than vague or verbose ones.	❌ "What should we do in NY?" -> ✅ "List 3 family attractions in Manhattan."
Be Specific About Output	Explicitly define output length, format, tone, or constraints.	"Write a 3-paragraph summary in JSON format."
Instructions > Constraints	Tell the model what to do, not what not to do.	✅ "List top consoles and their makers." vs ❌ "Don't mention video game names."
Control Token Length	Use model config or prompt phrasing to limit response length.	"Explain in 1 sentence" or set token limit.
Use Variables	Template prompts for reuse by inserting dynamic values.	Tell me a fact about {city}
Experiment with Input Style	Try different formats: questions, statements, instructions.	🔄 Compare: "What is X?", "Explain X.", "Write a blog about X."
Shuffle Classes (Few-Shot)	Mix up response class order to avoid overfitting to prompt pattern.	✅ Randomize class label order in few-shot tasks.
Adapt to Model Updates	LLMs evolve; regularly test and adjust prompts.	🔄 Re-tune for new Gemini / GPT / Claude versions.
Experiment with Output Format	For structured tasks, ask for output in JSON/XML to reduce ambiguity.	"Return response as valid JSON."
Document Prompt Iterations	Keep track of changes and tests for each prompt.	📝 Use a table or versioning system.

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🎯 Core Prompting Techniques

Technique	Description	Example Summary
Zero-Shot	Ask the model directly without any example.	🧠 "Classify this review as positive/neutral/negative."
One-Shot	Provide one example to show expected format/output.	🖋️ Input + Example -> New input
Few-Shot	Provide multiple examples to show a pattern.	🎓 Use 3-5 varied examples. Helps with parsing, classification, etc.
System Prompting	Set high-level task goals and output instructions.	🛠️ "Return the answer as JSON. Only use uppercase for labels."
Role Prompting	Assign a persona or identity to the model.	🎭 "Act as a travel guide. I'm in Tokyo."
Contextual Prompting	Provide relevant background info to guide output.	📜 "You're writing for a retro games blog."
Step-Back Prompting	Ask a general question first, then solve the specific one.	🔄 Extract relevant themes -> Use as context -> Ask final question
Chain of Thought (CoT)	Ask the model to think step-by-step. Improves reasoning.	🤔 "Let's think step by step."
Self-Consistency	Generate multiple CoTs and pick the most common answer.	🗳️ Run same CoT prompt multiple times, use majority vote
Tree of Thoughts (ToT)	Explore multiple reasoning paths in parallel for more complex problems.	🌳 LLM explores different paths like a decision tree
ReAct (Reason & Act)	Mix reasoning + action. Model decides, acts (e.g. via tool/API), observes, and iterates.	🤖 Thought -> Action -> Observation -> Thought
Automatic Prompting	Use LLM to generate prompt variants automatically, then evaluate best ones.	💡 "Generate 10 ways to say 'Order a small Metallica t-shirt.'"

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⚙️ LLM Output Configuration Essentials

Config Option	What It Does	Best Use Cases
Max Token Length	Limits response size by number of tokens.	📦 Prevent runaway generations, control cost/speed.
Temperature	Controls randomness of token selection (0 = deterministic).	🎯 0 for precise answers (e.g., math/code), 0.7+ for creativity.
Top-K Sampling	Picks next token from top K probable tokens.	🎨 Higher K = more diverse output. K=1 = greedy decoding.
Top-P Sampling	Picks from smallest set of tokens with cumulative probability ≥ P.	💡 Top-P ~0.9-0.95 gives quality + diversity.

🔁 How These Settings Interact

If You Set...	Then...
temperature = 0	Top-K/Top-P are ignored. Most probable token is always chosen.
top-k = 1	Like greedy decoding. Temperature/Top-P become irrelevant.
top-p = 0	Only most probable token considered.
high temperature (e.g. >1)	Makes Top-K/Top-P dominant. Token sampling becomes more random.

✅ Starting Config Cheat Sheet

Goal	Temp	Top-P	Top-K	Notes
🧠 Precise Answer	0	Any	Any	For logic/math problems, deterministic output
🛠️ Semi-Creative	0.2	0.95	30	Balanced, informative output
🎨 Highly Creative	0.9	0.99	40	For stories, ideas, writing