4 principles to master legal prompt engineering, with examples and suggestions for Paxton
Paxton is a cutting-edge legal assistant that utilizes natural language processing to digest complex legal documents. To get the most out of Paxton, we encourage you to practice the art of prompt engineering. A prompt is the question or instructions you provide to Paxton to complete a task. Prompts define the problem space and provide critical context to guide the AI.
Prompt engineering is the process of crafting and structuring prompts to optimize an AI system's performance. Well-crafted prompts lead to more accurate, relevant results from Paxton. Poorly constructed prompts are likely to result in vague, unfocused responses. While Paxton will always do its best to figure out what your prompt means, there are specific principles you should follow to get the best results.
To maximize Paxton's potential, grasping the art of legal prompt engineering is key. This guide covers:
Follow along as we unlock the secrets to prompting Paxton like a pro!
Before diving into examples, let's review three fundamental principles for engineering optimized prompts:
Craft clear, concise prompts that state your objectives directly. Eliminate any ambiguity. For example, many people ask, “Can you file a suit for …?” as a short hand for asking if a suit would be likely to succeed against a known affirmative defense. It’s better to just ask: “Would the suit be likely to succeed against [specific] affirmative defense?”
Equip Paxton with specifics like document names, dates, jurisdictions etc. to tailor the response. For example, if you have a tax law question about a business real estate swap, direct Paxton: “Make sure to check statutes about like-kind property for the specific rules governing this question.”
Provide necessary background details and explain the reasoning behind your prompt. Suppose you’re doing criminal defense research, and you want to learn what will make a state upgrade its charges for a burglary to a higher degree. If you’re interested in what the state considers a residence because your client burgled a barn, tell Paxton that the defendant burgled a barn so it can search for cases with a similar fact pattern.
Applying these principles ensures you get the most pertinent, accurate results from Paxton.
When starting out with Paxton, some common prompting mistakes include:
Dodging these missteps will noticeably enhance Paxton's output accuracy.
Leveraging templates lends consistency to your prompts. Consider these two versatile templates:
[Clarify Paxton's capabilities and constraints]
[Provide any essential background context]
[Specify prompt question for Paxton]
[Instruct on expected response format]
[Background context required to understand prompt]
[Detailed prompt for Paxton]
[Intended application of Paxton's generated response]
Now let's examine these templates applied through examples.
Let's contrast vague prompts with refined versions using best practices:
Summarize the main points of GDPR.
This prompt lacks specifics for Paxton to create a useful summary.
Could you outline the key rights granted to EU citizens under the General Data Protection Regulation (GDPR) in list format? This will help my legal team better understand the regulation. Focus on summarizing individuals' rights over their personal data.
The refined prompt equips Paxton with necessary details and context to generate a tailored summary.
Explain privacy laws in the United States.
This overly broad prompt could lead to a vague, unfocused answer.
Could you list and concisely explain the major federal laws and regulations pertaining to consumer privacy protections for digital services in the United States? I need this overview to draft a report on privacy regulations applicable to our products and customers in the US.
The enhanced prompt provides clarity, scope and purpose to elicit a relevant summary from Paxton.
Implementing these legal prompt engineering best practices will soon have you prompting like a Paxton pro! With practice, you'll gain mastery over the art of optimizing Paxton's capabilities.
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