# Prompt Engineering Strategies ## Elements - Explicit instructions: what we want the model to do - Question: formulating the task as a question - Context: Context within which the model performs the task - Role - Tone - etc ## Example instructions - You are a ... Write a short, persuasive text about the benefits of ... - You are a ... Reflect on ... - ... Provide advice on ... ## Zero-shot learning Most simple prompt engineering technique - Instructions: Describe the task - Input ## Few-shot learning Add more description to the prompt, so that the model can perform better (clearer context, goals, output restrictions, etc) Cons: - Uses a larger number of tokens - Charged per token - Conciseness is key ## Prompting by instruction Give explicit, clear directives to the model to guide its output Precision matters: - Direct commands - Output guidelines - eg tone, format Pros: - Precise control - Ideal for tasks with specific/expected outcomes - Less setup required Cons: - Requires clear and unambiguous language - May struggle with complex/nuanced tasks - Interpretation might not align with intent ## Prompting by example Provide examples and let the model figure out the goal and format Pros: - Can handle complex tasks, even if those require creativity - Reduced ambiguity if the input examples are good enough - Enables the model to learn from a variety of scenarios Cons: - Require high-quality examples for training - Time-consuming - Rigid model, too fixated on the given examples ## Chain-of-Thought prompting - Prompt, context, series of reasoning steps - Provides specific steps to follow - Enables the model to perform multiple steps and reason for each specifically - Discourages the model from generating quick answers This approach looks as follows: - Input > Prompt #1 > Output #1 > Prompt #2 > Output #2 > Prompt #3 > Final output Example - You are an AI assistant that helps customers book flights on the ... airline. F - Follow these steps to answer the customer queries - Step 1: First decide whether the user is asking a question about a specific airline or flight route - Step 2: ... - Step 3: If ... then ... - ... ## Multi-Chain Prompting Cfr [[Multi-Agent Workflow]]. ## Knowledge Augmentation Adding the following to prompts: - Relevant keywords and phrases - Structured data - Additional context - Access a vector database - Including search results - ... Helps the model perform better Can be combined with Chain-of-Thought and Multi-Chain prompting ## Prompt Lazy Loading Cfr [[Prompt Lazy Loading AI Design Pattern (PLL)]] ## References -