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ChatGPT Prompt Engineering for Developers

Introduction​

Two types of large language models (LLMs)

Base LLM​

Predicts next word, based on text training data

Prompt - What is the capital of France?

Ans - What is France's largest city?

Ans - What is France's population?

Instruction Tuned LLM​

Tries to follow instructions

Fine-tune on instructions and good attempts at following those instructions.

RLHF: Reinforcement Learning with Human Feedback - Human Feedback in AI: The Essential Ingredient for Success | Label Studio Create a High-Quality Dataset for RLHF | Label Studio

Helpful, Honest, Harmless

Prompt - What is the capital of France?

Ans - The capital of France is Paris.

Prompting Principles​

Prompts​

ChatGPT prompt to write error free​

{paste your writing}

Prompt: "Proofread my writing above. Fix grammar and spelling mistakes. And make suggestions that will improve the clarity of my writing"

Learn-fast prompt using the 80/20 principle to knowledge​

Prompt: "I want to learn about {insert topic}. Identify and share the most important 20% of learnings from this topic that will help me understand 80% of it."

Learning / Q&A / Test / Interview​

I'm currently learning about snowflake. Ask me a series of 50 questions, one at a time, that will test my knowledge. Wait for my response before proceeding to the next question, ask the next question after your explanation of the answers. Identify knowledge gaps in my answers and give me better answers to fill those gaps. When finish show me the quantity of correct answers and the quantity of failed answers

Create Test​

Create 30 MCQ (with 4 options each and 1 correct answer) for a finance intern for a 40 min test. The finance intern should have below skills

  • Ability to decode RBI guidelines
  • Proficiency in crafting easily understandable directives for tech teams
  • Previous IT experience is a plus

Questions should also focus on compliances, audits. Add 10 aptitude and logical reasoning questions. Add 5 data analytics using tables questions. Other 15 questions should be around - Certified banking compliance professional program.

Make sure to

  1. don't mention finance intern anywhere
  2. questions should be direct and with medium difficulty, the given questions are very easy and novice
  3. Share all 30 questions

Create 15 mcq questions (with 4 options each and 1 correct answer) around below topics

Presentation​

Create a presentation for presenting to top management of the company on title "Data Engineering". I as an owner to data engineering vertical working in service based company, have to tell the management about how we can create a Data Engineering vertical.

Start with importance of data and about the data industry. 1 one slide on what are different areas in Data like Data Engineering, Data Science, Data Analytics, ML, AI, etc. Then deep dive into data engineering. Also add on how we can start the vertical by building case studies, finding clients, checking competitive landscape. How much to invest and ROI, whom to hire and how many people to start with. What tools and technologies to focus on.

Presentation should be 30 mins long with 10-15 slides. I am aiming for a script that is persuasive, highlighting the different areas that can be tapped in Data Engineering, and how can we sell these as a service to other companies, and solve other company problems.

ChatGPT, could you aid me in crafting a compelling proposal presentation script for a project centered around integrating AI customer service solutions for Client's Name? I am aiming for a script that is persuasive, highlighting the advantages of our proposed solutions distinctively.

Principle 1: Write clear and specific instructions​

Tactic 1: Use delimiters to clearly indicate distinct parts of the input​
  • Delimiters can be anything like: , """, < >, <tag> </tag>, :
Tactic 2: Ask for a structured output​
  • JSON, HTML
Tactic 3: Ask the model to check whether conditions are satisfied​
Tactic 4: "Few-shot" prompting​

Principle 2: Give the model time to "think"​

Tactic 1: Specify the steps required to complete a task​
Tactic 2: Instruct the model to work out its own solution before rushing to a conclusion​

Imitating​

  • In the style of x write about x

Model Limitations​

Hallucinations​

Makes statements that sound plausible but are not true

Reducing hallucinations​

First find relevant information, then answer the question based on the relevant information

Other Topics​

  • Iterative
  • Summarizing
  • Inferring
  • Transforming
  • Expanding
  • Chatbot
  • Conclusion

ChatGPT Prompt Engineering for Developers - DeepLearning.AI

Prompting Techniques​

prompt-techniques

Chain-of-thought​

Chain-of-thought (CoT) prompting is a technique that allows large language models (LLMs) to solve a problem as a series of intermediate steps before giving a final answer. Chain-of-thought prompting improves reasoning ability by inducing the model to answer a multi-step problem with steps of reasoning that mimic a train of thought. It allows large language models to overcome difficulties with some reasoning tasks that require logical thinking and multiple steps to solve, such as arithmetic or commonsense reasoning questions.

Other techniques​

  • Generated knowledge prompting
  • Least-to-most prompting
  • Self-consistency decoding
  • Complexity-based prompting
  • Self-refine
  • Tree-of-thought
  • Maieutic prompting
  • Directional-stimulus prompting

Prompt engineering - Wikipedia

Prompt Examples​

ChatGPT Ultimate Prompting Guide

ChatGPT Prompts Commands

Share the most important leadership lessons and insights from the book {insert book} by {insert author}. For each insight suggest an actionable way I can embody it.

Parameters​

Temperature​

Controls the randomness of the model's output. A higher temperature makes the output more random, while a lower temperature makes it more deterministic.

Understanding OpenAI's Temperature Parameter | Colt Steele

Assistant APIs​

The Assistants API allows you to build AI assistants within your own applications. An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling.

At a high level, a typical integration of the Assistants API has the following flow:

  1. Create an Assistant in the API by defining its custom instructions and picking a model. If helpful, enable tools like Code Interpreter, Retrieval, and Function calling.
  2. Create a Thread when a user starts a conversation.
  3. Add Messages to the Thread as the user ask questions.
  4. Run the Assistant on the Thread to trigger responses. This automatically calls the relevant tools.

Create AI Assistants with OpenAI's Assistants API

Knowledge based retrieval tool -

platform.openai.com/docs/assistants/overview

Learning​