From: aidotengineer
Prompt engineering is a crucial aspect of working with large language models (LLMs), focusing on crafting effective inputs to achieve desired outputs [00:00:04]. Dan, co-founder of Prompt Hub, emphasizes its significance for anyone building AI-based features [00:01:00].
Why Prompt Engineering is Still Important
Despite a common misconception that one can “just tell the model to do” something, prompt engineering is far more nuanced in practice [00:00:57].
- Accessibility and Output Quality: It is the easiest and most accessible way to obtain better outputs from LLMs [00:01:15].
- Competitive Advantage: While everyone has access to the same models, the prompts, architecture, and surrounding systems provide a competitive advantage in product development [00:01:22].
- Simplicity and Management: Spending time to refine prompts can lead to much simpler and more manageable solutions compared to immediately resorting to complex methods like Retrieval Augmented Generation (RAG) [00:01:50]. If a problem can be solved via prompt engineering, it is often the more straightforward path [00:02:01].
Key Prompt Engineering Techniques
Various techniques and patterns are used to enhance LLM performance.
Chain of Thought Prompting
Chain of Thought prompting involves instructing the model to reason or “think about the problem” before providing an answer [00:02:27].
- Process: It breaks down problems into sub-problems, allowing insight into the model’s thinking process, which aids troubleshooting [00:02:38].
- Applicability: It is widely applicable across models and easy to implement [00:02:48].
- Implementation:
- Zero-shot: Adding phrases like “think step by step” or “take a breath and take it through” encourages reasoning [00:03:00].
- Few-shot: Providing examples of reasoning steps within the prompt can guide the model [00:03:17].
- Automatic Generation: LLMs can generate these reasoning chains themselves, such as through frameworks like Automatic Chain of Thought or AutoReason [00:03:30]. DeepSeek’s R1 model training also involved generating thinking processes within “think tags” [00:03:53].
- Use Case: Highly helpful for complex problems [00:04:31].
Few-Shot Prompting
Few-shot prompting involves including examples for the model to mimic or understand, effectively “showing rather than telling” [00:04:37].
- Benefit: This teaches the model a client’s tone or style through input/output examples (e.g., a brief and related content) [00:05:04].
- Efficiency: Most performance gains are achieved with just one or two examples [00:05:17]. Performance can even degrade with too many examples [00:05:28].
- Best Practice: Builders should select one or two diverse examples that cover different expected inputs [00:05:32].
Meta Prompting
Meta prompting is the practice of using LLMs to create, refine, or improve prompts [00:05:50].
- Frameworks and Tools: Various frameworks exist, some requiring voting knowledge, others more user-friendly [00:05:57]. Tools from Anthropic, OpenAI’s playground, and Prompt Hub offer this functionality [00:06:04].
- Model-Specific Optimization: Prompt Hub’s tool, for example, tailors the meta prompt based on the selected model provider (e.g., OpenAI vs. Anthropic) because optimal prompts can differ between models [00:06:14]. An iterative co-pilot feature allows users to run prompts and provide feedback [00:06:28].
Prompting Reasoning Models
Reasoning models like GPT-4 are fundamentally different in how they operate and how they should be prompted [00:06:51].
- Caution with Examples: Research by Microsoft (MedPrompt framework) and DeepSeek (R1 model) found that adding examples can lead to worse performance with these models [00:07:06]. OpenAI also cautioned that providing additional context can overcomplicate things and confuse the model [00:07:20]. If few-shot prompting is used, start with one or two examples at most [00:08:24].
- Encourage Reasoning: The more reasoning a model performs, the better its output can be [00:07:35]. Encouraging extended reasoning leads to better results and increased accuracy [00:07:51]. Phrases to encourage reasoning are often built into these models, so explicit instruction to reason might hurt performance [00:08:32].
- Minimal Prompting: For reasoning models, minimal prompting with a clear task description is often most effective [00:08:09].
Resources
Numerous free resources are available for prompt engineering, including Prompt Engineering Substack, blogs, and community prompts on platforms like Prompt Hub [00:08:41].