While there is no single, universally recognized “2nd hand” guide, a wealth of information exists on how to use AI effectively, ranging from foundational principles to advanced techniques. The key is adopting a strategic and iterative approach to your prompts, rather than viewing AI as a mind-reader.
Core principles for effective AI use
- View AI as a capable assistant, not an expert: AI is a tool for augmenting your skills, not a replacement for your own knowledge, critical thinking, and judgment. Always review and refine the output, especially for factual accuracy.
- Use it for brainstorming and initial drafts: AIs excel at generating a starting point, summarizing dense text, or providing fresh perspectives to overcome creative blocks. Use it to structure ideas, not to create the final product.
- Refine your queries iteratively: Your first prompt is rarely your best. Think of using AI as a conversation where you provide feedback to get closer to your desired outcome. If the result is off, give specific instructions for correction.
Advanced prompting techniques
1. Provide structure and context
Instead of a vague command, give the AI a clear framework to follow.
- Role-playing: Direct the AI to act as a specific persona, such as an expert copywriter, a financial advisor, or a specific historical figure, to frame its response.
- Constraints and examples: Define boundaries for the AI’s response. For instance, you can specify tone, length, format (like a table or JSON), or include examples to show it the exact style you want.
2. Encourage deeper reasoning
For complex or multi-step tasks, ask the AI to show its work.
- Chain-of-Thought (CoT): This technique, developed by Google researchers, asks the AI to break down its reasoning step-by-step. This improves accuracy and allows you to follow its logic to catch errors.
- Tree of Thoughts (ToT): An extension of CoT, ToT prompts the model to explore multiple reasoning paths in parallel and evaluate them before providing the final answer. This is useful for planning or complex problem-solving.
- Generated knowledge: Ask the AI to first generate relevant facts or context about a topic before answering your question.
3. Build a system, not a single prompt
Productive AI use involves a multi-prompt strategy.
- Sequential prompting: Build up complexity over multiple conversational turns rather than trying to get everything from one initial prompt. This allows for clarification and refinement.
- “Step-back” prompting: Ask the AI a foundational, high-level question to build context before asking for the specific output.
- Document and reuse: Create a “prompt library” of your most effective query frameworks so you can quickly apply them to new tasks.
Best practices for working with AI
- Fact-check everything: AI models can sometimes “hallucinate” or invent facts. Always verify important information with a reputable external source. The SIFT technique (Stop, Investigate the source, Find better coverage, Trace claims) is a reliable method.
- Start small and scale: Use pilot projects to test and evaluate AI tools before integrating them broadly into your workflow.
- Be mindful of data privacy: Do not submit any sensitive, private, or confidential information to a public AI model. Establish clear policies on what data can be used.
- Consider ethical implications: Be aware of potential biases in AI outputs, as models are trained on data that can reflect existing societal biases. This is particularly important for sensitive applications like hiring or law enforcement.