Prompting 101

Course link: https://learn.deeplearning.ai/courses/chatgpt-prompt-eng/lesson/1/introduction

1. Contents

   • Best practices
   • Common use cases

2. Types of LLMs

   1. Base: Predicts next work based on text training data

   2. Instruction tuned

      • Follows instructions; fine tuned on instructions.
      • Trained on inputs and outputs
      • Uses RLHF - Reinforcement Learning with Human Feedback
        • Helpful, Honest, Harmless
      • Recommended for most practical use cases
      • While using, think of giving instructions to another human
        • Quantity of information tailored to the kind of response expected is optimum
      • Be clear and specific

3. Guidelines for prompting - Principals and Tactics

   • Write clear and specific instructions
   • Doesn’t mean it has to be short - longer prompts can be better and provide more insights.

4. Tactics

   1. Use delimiters to clearly indicate distinct parts of the input
      • Delimiters also help with avoiding prompt injection
   2. Ask for a structured output
      • Provide an output format where feasible
   3. Ask model to check whether conditions are satisfied
      • Conditional prompt (if..else)
      • Any assumptions can yield wrong outcome
      • Exit early after checking conditions
   4. Few-shot prompting
      • Providing successful examples of (part of) tasks to be performed - “What does success look like?”
   5. Give model time to ‘think’
      • Complex tasks can take a long time/computation. Tell the model to take more time to get an answer

5. Tactics

   1. Specify steps required to complete task
      • Ask for output in specific format
   2. Instruct the model to work out it’s own solution before rushing to solution
      • Ask to do it’s own work, then compare and evaluate - “Do not decide if solution is correct until you’ve done the problem yourself”.

6. Model limitations

   1. Hallucinations - making statements that sound plausible but aren’t true
      • Known weakness of models at current time

7. Iterative prompt development

   1. First prompt to solve a problem rarely works the first time
   2. Iterate and get closer to the desired result
      • Refine with a batch of examples
   3. Be precise and clear
   4. Giving a role and task can help

8. Common use cases

   1. Summarizing text
      • Giving purpose helps generate better results (more context)
      • Limit by sentences/words.
        • Doesn’t always adhere to provided limit
        • Character limiting rarely works due to tokenization mechanism
   2. Inferring
      • Making sense of sentiment - whether something is positive or negative
      • LLMs are good at extracting information from a info source
      • “Zero-shot learning”
   3. Transforming
   4. Expanding
      • Temperature
        • Lower temperature (0), more reliability, predictability
        • Higher temp yields more variety (randomness, creativity)

Self notes

1. Maybe working backwards from expected result would work coming up with proper requirements