Session brief
Prompt Engineering Tactics: Dan Cleary
Overview
This talk introduces three practical prompt engineering tactics designed to improve the accuracy and reliability of responses from large language models (LLMs). The presenter emphasizes that while basic prompts often yield good results, advanced techniques are crucial for integrating AI into products, ensuring consistent user experiences, and meeting high user expectations for speed, accuracy, and freedom from hallucinations. These methods are applicable for both casual use of LLMs and for developers building AI-powered applications.
Who should watch
- AI Engineers
- Product Managers
- Builders integrating AI into products
- Anyone seeking to improve LLM response quality and consistency
- Users experiencing unpredictable or off-topic LLM outputs
Key takeaways
- Prompt engineering is vital for AI integration to ensure brand trust and meet user expectations for crisp, accurate, and hallucination-free outputs.
- *Multipersona prompting* involves assigning specific roles or personas to the LLM to collaborate on a task, which is particularly useful for complex or generative tasks.
- The *according to method* grounds prompts to a specific source by appending phrases like "according to [source]", which can reduce hallucinations by up to 20% and is effective with fine-tuned or consistently sourced models.
- *Motion prompting* leverages emotional stimuli at the end of a prompt to encourage more serious consideration by the LLM, potentially increasing output quality and accuracy by 8% to 115% depending on the task.
- Small changes in prompt wording can have significant effects on LLM outputs due to the non-deterministic nature of these models.
- These tactics can be implemented in everyday LLM use or within AI features integrated into products.
Notable quotes
*The non-deterministic nature of these models makes it really hard to predict and I've seen that having little changes in a prompt can have outsize effect on the outputs.*
*Users now that we all have access to chat gbt and can really easily access these models we have very high expectations when we're using AI features inside of products.*
Unofficial community note. Prefer the recording for nuance.