πŸ˜ƒ Basics
🧠 Advanced
Zero-Shot
🟒 Introduction
🟒 Emotion Prompting
🟒 Role Prompting
🟒 Re-reading (RE2)
🟒 Rephrase and Respond (RaR)
🟦 SimToM
β—† System 2 Attention (S2A)
Few-Shot
🟒 Introduction
🟒 Self-Ask
🟒 Self Generated In-Context Learning (SG-ICL)
🟒 Chain-of-Dictionary (CoD)
🟒 Cue-CoT
🟦 Chain of Knowledge (CoK)
β—† K-Nearest Neighbor (KNN)
β—†β—† Vote-K
β—†β—† Prompt Mining
Thought Generation
🟒 Introduction
🟒 Chain of Draft (CoD)
🟦 Contrastive Chain-of-Thought
🟦 Automatic Chain of Thought (Auto-CoT)
🟦 Tabular Chain-of-Thought (Tab-CoT)
🟦 Memory-of-Thought (MoT)
🟦 Active Prompting
🟦 Analogical Prompting
🟦 Complexity-Based Prompting
🟦 Step-Back Prompting
🟦 Thread of Thought (ThoT)
Ensembling
🟒 Introduction
🟒 Universal Self-Consistency
🟦 Mixture of Reasoning Experts (MoRE)
🟦 Max Mutual Information (MMI) Method
🟦 Prompt Paraphrasing
🟦 DiVeRSe (Diverse Verifier on Reasoning Step)
🟦 Universal Self-Adaptive Prompting (USP)
🟦 Consistency-based Self-adaptive Prompting (COSP)
🟦 Multi-Chain Reasoning (MCR)
Self-Criticism
🟒 Introduction
🟒 Self-Calibration
🟒 Chain of Density (CoD)
🟒 Chain-of-Verification (CoVe)
🟦 Self-Refine
🟦 Cumulative Reasoning
🟦 Reversing Chain-of-Thought (RCoT)
β—† Self-Verification
Decomposition
🟒 Introduction
🟒 Chain-of-Logic
🟦 Decomposed Prompting
🟦 Plan-and-Solve Prompting
🟦 Program of Thoughts
🟦 Tree of Thoughts
🟦 Chain of Code (CoC)
🟦 Duty-Distinct Chain-of-Thought (DDCoT)
β—† Faithful Chain-of-Thought
β—† Recursion of Thought
β—† Skeleton-of-Thought
πŸ”“ Prompt Hacking
🟒 Defensive Measures
🟒 Introduction
🟒 Filtering
🟒 Instruction Defense
🟒 Post-Prompting
🟒 Random Sequence Enclosure
🟒 Sandwich Defense
🟒 XML Tagging
🟒 Separate LLM Evaluation
🟒 Other Approaches
🟒 Offensive Measures
🟒 Introduction
🟒 Simple Instruction Attack
🟒 Context Ignoring Attack
🟒 Compound Instruction Attack
🟒 Special Case Attack
🟒 Few-Shot Attack
🟒 Refusal Suppression
🟒 Context Switching Attack
🟒 Obfuscation/Token Smuggling
🟒 Task Deflection Attack
🟒 Payload Splitting
🟒 Defined Dictionary Attack
🟒 Indirect Injection
🟒 Recursive Injection
🟒 Code Injection
🟒 Virtualization
🟒 Pretending
🟒 Alignment Hacking
🟒 Authorized User
🟒 DAN (Do Anything Now)
🟒 Bad Chain
πŸ”¨ Tooling
Prompt Engineering IDEs
🟒 Introduction
GPT-3 Playground
Dust
Soaked
Everyprompt
Prompt IDE
PromptTools
PromptSource
PromptChainer
Prompts.ai
Snorkel 🚧
Human Loop
Spellbook 🚧
Kolla Prompt 🚧
Lang Chain
OpenPrompt
OpenAI DALLE IDE
Dream Studio
Patience
Promptmetheus
PromptSandbox.io
The Forge AI
AnySolve
Conclusion
πŸ’Ό Applications🟒 Writing Emails

Writing Emails

🟒 This article is rated easy
Reading Time: 3 minutes
Last updated on March 10, 2025

Sander Schulhoff

Takeaways
  • Use AI to write emails: AI can help draft emails, from sick-day notifications to cold outreach messages.
  • Customize emails: Provide specific prompts to create emails that fit the intended tone and audience.
  • Summarize long emails: AI can summarize lengthy emails and list action items, helping you focus on key responses.
  • Personalize cold outreach: Adding details from LinkedIn profiles to prompts can make cold emails more relevant and increase response rates.

Writing emails can be time-consuming, especially when you need to read through an email you received first. This section will cover use cases ranging from a simple email telling your boss you are sick today to more complex cold lead emails.

Basic Sick Day Email

Imagine you wake up sick one day and can't go to work (or don't want to 😈). Here is a simple prompt that writes an email to your boss telling them you are sick.

This email works but is pretty boring. Let's spice it up a bit!

Style Modifiers/Instructions

It is very easy to modify the style of the email. For example, you can ask the AI to be 'humorous' or instruct it to 'Include a funny reason'.


Here is another example that is more serious/professional.


Responding to an Email

Imagine that you receive a long email from your boss with a lot of information. You need to respond to the email, but you don't have time to read through the entire thing. You can plug the email into an AI and ask it to Generate a summary of this and a list of action items.


You can then use this summary to write a response email.


Note that you can often combine these two steps into one. You can ask the AI to generate a response email directly from the email you received.

Cold Emails

Cold emails are emails sent to people that you don't know. It is difficult to get a response from cold emails, so it can be helpful to send out a lot of personally customized emails. Let's see how to do that with GPT-3.

This is neat, but we can do better. Let's add some more information to the prompt.

Using unstructured information

Say you have the LinkedIn profile of the person you are sending the email to. You can add that information to the prompt to make the email more personalized. Let's use the founder of Strive's LinkedIn as an example. We'll copy a bunch of information from his profile and add it to the prompt.

It is super useful that Large Language Models (LLMs) can cut through the clutter of all the information we copied from LinkedIn. This cold outreach process can also be automated at a larger scale by scraping LinkedIn for relevant information.

Conclusion

LLMs can help you write emails! Make sure you read what they write before sending it πŸ˜‰

Footnotes

  1. Bonta, A. (2022). How to use OpenAI’s ChatGPT to write the perfect cold email. https://www.streak.com/post/how-to-use-ai-to-write-perfect-cold-emails ↩

Sander Schulhoff

Sander Schulhoff is the Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.