Session brief
The Model Isn’t Wrong—You’re Just Bad at Prompting
Overview
This talk focuses on the nuances of prompt engineering, arguing that effective prompting is crucial for achieving optimal outputs from large language models. It emphasizes that while models are powerful, user-defined prompts significantly influence performance, offering a competitive advantage in product development. The core thesis is that mastering prompt engineering techniques is essential for builders and engineers working with AI.
Who should watch
- AI Engineers
- Product Managers
- Builders working with LLMs
- Anyone seeking to improve AI model outputs
- Those facing challenges in getting desired results from AI models
Key takeaways
- Prompt engineering remains a vital skill, offering the most accessible way to improve LLM outputs and providing a competitive edge.
- Chain of Thought (CoT) prompting, which instructs models to reason step-by-step before answering, is highly effective for breaking down complex problems and aiding in troubleshooting.
- Few-shot prompting involves providing examples for the model to mimic, with significant performance gains often achieved with just one or two diverse examples.
- Meta-prompting, using LLMs to generate, refine, or improve prompts, is a powerful technique, with various tools available to assist in this process.
- Reasoning models behave differently and require different prompting strategies; excessive few-shot examples can sometimes degrade performance.
- For reasoning models, clear task descriptions and encouraging more explicit reasoning steps can lead to better accuracy and performance.
- Avoid extensive few-shot prompting with reasoning models; start with one or two examples if necessary, as their reasoning capabilities are often built-in.
- Numerous free resources and templates are available for prompt engineering techniques, including Chain of Thought and meta-prompting tools.
Notable quotes
*Prompt engineering is a part of a greater system. We all have access to the same models, but the prompts, the architecture, everything around that is how we can also have a competitive advantage.*
*It's really easy to kind of run away with these things when you're working with LLMs. Think about everything you can do, and that's great, but you know, just spending an hour trying to mess around with the prompt and then determining that it's impossible to solve whatever you're trying to do via prompt engineering and you need to do a complex RAG or things along those lines, I think is it's not super smart.*
*Overall, when you're using reasoning models specifically, minimal prompting, nothing can really be like a really good clear task description. Want to encourage more reasoning.*
Unofficial community note. Prefer the recording for nuance.