Prompt Engineering

Enhance results with prompt engineering strategies.

This guide shares strategies and tactics for getting better results from large language models. The methods described here can sometimes be deployed in combination for greater effect. We encourage experimentation to find the methods that work best for you.

Strategies for Getting Better Results

1. Write Clear Instructions

These models can’t read your mind. If outputs are too long, ask for brief replies. If outputs are too simple, ask for expert-level writing. If you dislike the format, demonstrate the format you’d like to see. The less the model has to guess at what you want, the more likely you’ll get it.

Tactics:

• Include details in your query to get more relevant answers
• Ask the model to adopt a persona
• Use delimiters to clearly indicate distinct parts of the input
• Specify the steps required to complete a task
• Provide examples
• Specify the desired length of the output

2. Provide Reference Text

Language models can confidently invent fake answers, especially when asked about esoteric topics or for citations and URLs. Providing reference text to these models can help in answering with fewer fabrications.

Tactics:

• Instruct the model to answer using a reference text
• Instruct the model to answer with citations from a reference text

3. Split Complex Tasks into Simpler Subtasks

Complex tasks tend to have higher error rates than simpler tasks. Complex tasks can often be re-defined as a workflow of simpler tasks.

Tactics:

• Use intent classification to identify the most relevant instructions for a user query
• For dialogue applications that require very long conversations, summarize or filter previous dialogue
• Summarize long documents piecewise and construct a full summary recursively

4. Give the Model Time to "Think"

Models make more reasoning errors when trying to answer right away, rather than taking time to work out an answer. Asking for a "chain of thought" before an answer can help the model reason its way toward correct answers more reliably.

Tactics:

• Instruct the model to work out its own solution before rushing to a conclusion
• Use inner monologue or a sequence of queries to hide the model's reasoning process
• Ask the model if it missed anything on previous passes

5. Use External Tools

Compensate for the weaknesses of the model by feeding it the outputs of other tools. For example, a text retrieval system (sometimes called RAG or retrieval augmented generation) can tell the model about relevant documents.

Tactics:

• Use embeddings-based search to implement efficient knowledge retrieval
• Use code execution to perform more accurate calculations or call external APIs
• Give the model access to specific functions