Over the weekend I watched the quick course put out by Andrew Ng and Isa Fulford about ChatGPT Prompt Engineering for developers. In the video series, they showed a lot of examples that inspired me to come up with my own application ideas, so I definitely recommend watching the course. It took me around 2 hours to finish the whole series. Below are notes for quick reference and reminders.

Types of LLMs

Base LLM vs Instruction Tuned LLM

  • Base LLM: predicts next word, based on text training data

    • Example:

      • Input: What is the capital of France?

      • Output: What is France’s largest city? What is France’s population?

  • Instruction Tuned LLM: tires to follow instructions

    • Example:

      • Input: What is the capital of France?

      • Output: The capital of France is Paris.

    • Start off with a base LLM that’s been trained on a huge amount of text data and further fine tune it with inputs and outputs that are instructions and good attempts to follow these instructions

    • Then further refined by RLHF -> Reinforcement learning with human feedback

    • Recommended for most applications to be deveoped

Guideline for Prompting

First principle: give clear and specific instructions

  • Clear \(\neq\) Short

  • Tactic 1: Use delimiters to clearly indicate distinct parts of the input

    • Delimiters examples: ```, “““, <>, <tag> </tag>, : , —

    • Helps avoid prompt injections as well

  • Tactic 2: Ask for a structured output (i.e. JSON, HTML)

  • Tactic 3: Ask the model to check whether conditions are satisfied

  • Tactic 4: Few-shot prompting -> Give successful examples of completing tasks then ask model to perform the task

Second principle: give the model time to think

  • Tactic 1: Specify the steps required to complete a task and ask for output in a specified format

  • Tactic 2: Instruct the model to work out its own solution before rushing to a conclusion

Model Limitations:

  • Hallucination: makes statements that sound plausible but are not true

    • Reducing hallucinations: first find relevant information, then answer the question based on the relevant information

Iterative Prompt Development

Prompt guidelines

  • Be clear and specific the first time

  • Analyze why result does not give desired output

  • Clarify instructions, give more time to think

  • Refine the idea and the prompt with a batch of examples -> build on the initial prompt

  • Repeat

iterative process

Summarizing

  • Could ask the model to summarize with specific requirements such as word limits and focus area.

  • Could also extract instead of summarize (summarize may include more information than asked for)

  • Could put a list of similar texts in a list and use a loop to iterate through the list to do the same type of summarizing

Inferring

  • Given a paragraph, infer emotions, or answer questions with specific requirements

  • Infer topics and could also determine if the text talks about a certain topic

Transforming

  • Can be used to do common transformation tasks such as translation, language detection

  • Could build a universal translator by iterating through messages in different languages in a loop

  • Could do tone transformation, format conversion (HTML -> JSON, etc.)

  • Could use spellcheck/grammar check

    • Cool library to use to display the edited version with red lines indicating the changes from the original version:

        from redlines import Redlines
  diff = Redlines(text, response)
  display(Markdown(diff.output_markdown))

Expanding

  • Could expand on existing content based on prompt

  • Temperature: degree of exploration or randomness of the model

    • Temperature = 0: the model will always choose the most likely next word

    • Temperature higher: will choose more of the less likely next word

Chatbot

  • Build your own chatbot: instead of only giving ChatGPT one message at a time, give it multiple messages and specify roles in JSON format:

      messages = [
      {'role': 'system', 'content': 'You are a friendly chatbot.'}, 
      {'role': 'user', 'content': 'tell me a joke'}, 
      {'role': 'assistant', 'content': 'Why did the chicken cross the road'}, 
      {'role':'user', 'content':  'i don\'t know'} 
      ]

  • Add context -> write a helper function that automatically collects context and add input and output responses from the user and chatbot interaction (i.e. adding to the messages list)