You already know that Google dropped a 68-page guide on advanced prompt engineering
Solid stuff! Highly recommend reading it
BUT… if you don’t want to go through 68 pages, I have made it easy for you
.. By creating this Cheat Sheet
A Quick read to understand various advanced prompt techniques such as CoT, ToT, ReAct, and so on
The sheet contains all the prompt techniques from the doc, broken down into:
✅ Prompt Name
✅ How to Use It
✅ Prompt Patterns (like Prof. Jules White's style)
✅ Prompt Examples
✅ Best For
✅ Use cases
It’s FREE. to Copy, Share & Remix
Go download it. Play around. Build something cool
https://cognizix.com/prompt-engineering-by-google/
Videos
Recently stumbled upon a really cool paper from Vanderbilt University: A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.
Sent me down the rabbit hole of prompt patterns (like, what they even are etc), which lead me to putting together this post with 16 free templates and a Gsheet.
Generally, prompt patterns are high-level solutions to recurring problems.
I copied the first 6 below, but the other 10 are in the post above.
I've found these to be super helpful to visit whenever running into a prompting problem. Hope they help!
Prompt pattern #1: Meta language creation
Intent: Define a custom language for interacting with the LLM.
Key Idea: Describe the semantics of the alternative language (e.g., "X means Y").
Example Implementation: “Whenever I type a phrase in brackets, interpret it as a task. For example, '[buy groceries]' means create a shopping list."
Prompt pattern #2: Template
Intent: Direct the LLM to follow a precise template or format.
Key Idea: Provide a template with placeholders for the LLM to fill in.
Example Implementation: “I am going to provide a template for your output. Use the format: 'Dear [CUSTOMER_NAME], thank you for your purchase of [PRODUCT_NAME] on [DATE]. Your order number is [ORDER_NUMBER]'."
Prompt pattern #3: Persona
Intent: Provide the LLM with a specific role.
Key Idea: Act as persona X and provide outputs that they would create.
Example Implementation: “From now on, act as a medical doctor. Provide detailed health advice based on the symptoms described."
Prompt pattern #4: Visualization generator
Intent: Generate text-based descriptions (or prompts) that can be used to create visualizations.
Key Idea: Create descriptions for tools that generate visuals (e.g., DALL-E).
Example Implementation: “Create a Graphviz DOT file to visualize a decision tree: 'digraph G { node1 -> node2; node1 -> node3; }'."
Prompt pattern #5: Recipe
Intent: Provide a specific set of steps/actions to achieve a specific result.
Example Implementation: “Provide a step-by-step recipe to bake a chocolate cake: 1. Preheat oven to 350°F, 2. Mix dry ingredients, 3. Add wet ingredients, 4. Pour batter into a pan, 5. Bake for 30 minutes."
Prompt pattern #6: Output automater
Intent: Direct the LLM to generate outputs that contain scripts or automations.
Key Idea: Generate executable functions/code that can automate the steps suggested by the LLM.
Example Implementation: “Whenever you generate SQL queries, create a bash script that can be run to execute these queries on the specified database.”