1. Zero-Shot Prompting
- Description: Instruct the model to perform a task with only a clear instruction, no examples provided.
- Example: “Summarize this article in three sentences.”
- Best for: Quick tasks, when examples aren’t needed.
2. Few-Shot Prompting
- Description: Provide the model with a few input-output examples so it can infer the pattern.
- Example: “Translate to French: ‘Good morning’ = ‘Bonjour’. ‘How are you?’ = ‘Comment ça va?’ Now translate: ‘Thank you.’”
- Best for: Tasks where context or format is important.
3. Chain-of-Thought Prompting
- Description: Ask the model to reason step-by-step, improving complex or multi-step answers.
- Example: “Let’s think step by step: What happens when you mix vinegar and baking soda?”
- Best for: Reasoning, math, logic, and multi-step tasks.
4. ReAct (Reason + Act) Framework
- Description: Prompts the model to alternate between reasoning and taking actions, often used for agentic workflows.
- Example: “Think: What information do I need? Act: Search for the author’s biography. Think: What did I find?”
- Best for: Tool-using agents, research, and interactive tasks.
5. Prompt Chaining
- Description: Combine multiple prompts or model outputs in sequence to build complex workflows.
- Example: “Summarize this article. Now, turn the summary into a tweet. Next, generate hashtags.”
- Best for: Multi-stage tasks, content pipelines, automation.
6. Role-Task-Format (RTF) Framework
- Description: Clearly define the model’s role, the task, and the desired output format.
- Example: “You are a travel guide. List five must-see sights in Paris as bullet points.”
- Best for: Ensuring clarity and consistency in responses.
7. LangChain
- Description: Open-source framework for building modular, multi-step LLM workflows using prompt templates, memory, agents, and chains.
- Best for: Developers building advanced AI applications, chatbots, or document processors.
8. OpenPrompt
- Description: Modular framework for designing, testing, and evaluating prompt templates with dynamic variables and advanced context management.
- Best for: Research, large-scale prompt evaluation, and template libraries.
9. PromptLayer
- Description: Framework for tracking, versioning, and analyzing prompt performance at scale, with analytics and monitoring tools.
- Best for: Teams managing many prompts or enterprise AI deployments.
10. Guidance
- Description: Library for constraining and validating LLM outputs using regex, grammars, and custom tools, ensuring outputs meet specific rules.
- Best for: Structured data extraction, API integration, and applications needing strict output formats.