4.6 Ethical Considerations in Prompt Engineering

Ethical Boundaries of Intelligent Interaction: Responsibility and Prudence in Prompt Engineering

Prompt Engineering is not merely a technical practice aimed at improving the output quality of Large Language Models (LLMs); it also carries profound and unavoidable ethical responsibilities. Every prompt we carefully design, or even casually input, acts like an instruction given to an “intelligent agent” possessing astonishing capabilities but lacking a well-developed value system, common sense judgment, or even a deep understanding of real-world consequences. These instructions not only directly shape the quality, style, and superficial accuracy of the LLM’s output but can also inadvertently—or in rare malicious cases, intentionally—guide the model to generate content that is systemically biased, distorts truth, violates privacy, spreads harmful information, or potentially even breaks laws and regulations.

For professionals in the legal industry—a field highly dependent on public trust, dedicated to the pursuit of fairness and justice, and bound by extremely strict duties of confidentiality—fully recognizing, deeply understanding, and prudently handling the inherent ethical considerations when exploring and utilizing prompt engineering to unlock AI’s amazing potential is not an optional “ethical label” or a gesture of “political correctness.” It is an absolute requirement and core professional competency directly related to maintaining our professional reputation, fulfilling our fiduciary duties to clients and society, and ensuring that technology ultimately serves good (AI for Good) rather than abetting harm (AI for Bad).

This section delves into the critical ethical boundaries and non-delegable responsibilities that legal professionals must remain vigilant about, seriously confront, and thoughtfully consider when engaging in prompt engineering.

I. Avoiding Bias Induction and Amplification: Beware the “Colored Glasses” AI Might Wear

The knowledge and capabilities of Large Language Models (LLMs) stem from the massive training data they learn from, primarily sourced from the internet and digitized books. This data, like a mirror reflecting the real world, inevitably absorbs and reflects various explicit and implicit biases long present in human society. These biases can relate to gender, race, ethnicity, region, age, religion, sexual orientation, socioeconomic status, physical characteristics, occupation, and many other aspects, often manifesting as Stereotypes, Systemic Discrimination, or Under-representation within the data.

Although AI model developers invest significant effort into Alignment techniques—such as carefully filtering and cleaning training data, conducting supervised Instruction Fine-tuning, employing Reinforcement Learning from Human Feedback (RLHF) or AI Feedback (RLAIF)—to attempt to detect, assess, and mitigate these latent internal biases, they often cannot be completely eliminated due to the deep-rooted, complex, subtle nature of bias and its intricate entanglement with society and culture.

Furthermore, poorly designed, ill-considered, or inherently biased prompts can unintentionally “activate,” “trigger,” or even “cater to” these latent biases within the model. In some cases, AI might even amplify (Bias Amplification) subtle biased signals in the prompt, even under seemingly neutral instructions, ultimately leading to generated text content that carries clear discriminatory undertones, unfair tendencies, or simply reinforces harmful social stereotypes.

• Specific Ethical Risks:

  • Generating Discriminatory or Stereotypical Text: If a prompt contains unexamined, biased assumptions (e.g., “Analyze why [a certain group] is often more aggressive/passive in negotiations”), uses words or describes scenarios implying specific stereotypes, or poses overly leading questions that presuppose an answer’s direction, the LLM is likely to “go with the flow,” generating text that directly or indirectly reinforces these stereotypes or clearly discriminates against specific groups.
    • High-Risk Prompt Example: Asking the model to “Write a story about a ‘cunning merchant’ whose main character is [a specific ethnicity]” (high risk of generating racist content); or “Explain why women might be better suited for handling family law matters?” (reinforces gender stereotypes, ignores individual capabilities).
  • Leading to Unfair Analysis or Assessment Results: When using AI for tasks like legal analysis, risk assessment, case value prediction, or preliminary evidence credibility judgment, if the prompt design intentionally or unintentionally emphasizes or ignores factors related to protected characteristics (gender, race, socioeconomic status), or more subtly, uses Proxy Variables that appear neutral but are highly correlated with sensitive attributes (e.g., over-relying on zip codes in recidivism risk assessment, or considering university reputation in loan applications), then the conclusions generated by AI based on this biased input or logic can be systematically disadvantageous to certain groups, leading to de facto unfairness and potentially violating anti-discrimination laws.
  • Exacerbating the Digital Divide and Information Inequality: If common prompt design patterns overly rely on complex legal jargon, specific cultural background knowledge, or an “elitist” expression style requiring higher education levels to understand and use, then user groups lacking corresponding legal literacy, language skills, or cultural background (e.g., individuals with lower education levels, minority language speakers, elderly unfamiliar with technology) might find it difficult to effectively use these AI tools to access desperately needed legal information or preliminary services. This not only limits the realization of AI’s inclusive value but could even worsen societal inequality in information access, utilization, and access to legal services.

• Ethical Countermeasures & Responsible Design Strategies in Prompt Engineering:

  • Use Neutral, Objective, and Inclusive Language in Prompts: When designing and writing prompts, legal professionals must constantly maintain high sensitivity, consciously review and strive to avoid using any words, presuppositions, qualifying descriptions, or scenario settings that might carry bias related to gender, race, age, region, disability, or other forms, or could evoke stereotypes. Aim for objective, neutral, precise, and inclusive language.
  • Actively Emphasize Fairness and Neutrality Requirements in Prompts: For tasks involving sensitive topics or value judgments, proactively include explicit instructional statements in the prompt, requiring the model to “maintain an objective and neutral stance, avoiding any form of biased or discriminatory statements,” “ensure your analysis and conclusions are fair to all relevant parties,” “consider this issue from a perspective of diversity and inclusion,” or “personal identity characteristics irrelevant to the law should not be considered in the analysis.” This helps guide the model towards more responsible output.
  • Carefully Design Persona Prompt Details: When using role-playing prompts to enhance professionalism, pay special attention to avoiding personas that might entrench or reinforce social stereotypes. For example, avoid inappropriately associating specific genders with specific legal fields or practice abilities (e.g., “a successful male business lawyer” is less neutral than “an experienced business lawyer”). Strive to use role descriptions based on professional knowledge, experience level, and skill sets rather than identity characteristics.
  • Periodically Assess Common Prompt Templates for Bias Risks: For standardized prompt templates or key prompts widely used within an organization (e.g., for initial contract review, case summarization, client inquiry classification), establish mechanisms for regular review and specific testing to check if they might systematically trigger biased outputs when processing data involving different groups or situations. If issues are found, promptly revise, iterate, provide risk warnings, or update training for users.
  • Be Aware of Bias in Input Data and Interpret Output Cautiously: Recognize deeply that even if your prompt is perfectly neutral, if the input data provided to the model (e.g., case descriptions, client information, historical case law database) itself contains systemic bias or discriminatory information, the model’s output is highly likely to be contaminated by this “toxic” data. Therefore, when interpreting and applying AI analysis results based on such data, exercise extreme caution and critical thinking, proactively considering and attempting to correct for the potential impact of data bias.

• Example Comparison (Illustrating how to avoid inducing bias):

  • Prompt with Bias Risk: “Analyze why parties from [a specific economically disadvantaged region] are often at a disadvantage in complex commercial litigation? What necessary legal knowledge and resources might they lack?” (This prompt presupposes disadvantages based on region and may lead to oversimplified attributions based on regional discrimination or stereotypes.)
  • More Neutral Prompt Focusing on Structural Issues: “In complex commercial litigation, what are the common structural barriers that might impact a party’s ability to effectively participate, regardless of their background? For example, what are the general challenges related to accessing high-quality legal representation, understanding complex legal procedures, collecting and organizing evidence, and affording high litigation costs? Please analyze based on relevant systems and practices.” (This prompt focuses the question on universal structural barriers rather than presupposing disadvantages for a specific group, thus guiding towards a more objective and constructive analysis.)

Use Fairness Toolkits for Auxiliary Assessment

Research institutions and major tech companies are developing toolkits for detecting and quantifying AI model bias (Fairness Toolkits), such as Google’s What-If Tool, IBM’s AI Fairness 360, Microsoft’s Fairlearn. While currently perhaps more oriented towards AI developers and researchers, awareness of their existence and related research progress helps enhance legal professionals’ sensitivity, understanding, and assessment capabilities regarding algorithmic bias issues.

II. Ensuring Output Transparency and Accountability: Opening a Window into AI’s “Black Box”

The internal workings of Large Language Models (LLMs) are extremely complex, often resembling an opaque “black box” (see Section 2.8). How the model arrives at a specific answer from the input prompt and its vast internal parameters—its deep “thinking” path and basis—is often difficult for humans to fully understand, precisely trace, or clearly explain. This severe lack of Explainability / Interpretability poses immense challenges to the legal field, which highly values reasoned argumentation, procedural transparency, and clear accountability.

If a critical legal judgment or decision relies heavily on an “unexplainable” black box AI, its legitimacy, reasonableness, and reliability are fundamentally questioned. While prompt engineering cannot completely crack open this “black box,” through clever design and specific output requirements, we can, to some extent, enhance the transparency of the AI’s output process, providing more clues and basis for subsequent human review, fact-checking, and responsibility attribution.

• Ethical Risks:

  • Blind Trust Leads to Vacated Responsibility: Users (lawyers, judges, clients) might, due to the fluency, structure, confident tone, and seemingly professional citations/terminology in LLM output, over-rely on its accuracy and reasonableness, thereby abdicating their responsibility for independent critical thinking and rigorous fact-checking. When serious errors in AI output eventually lead to adverse consequences, users might subconsciously blame “what the AI said” or “the algorithm,” attempting to deflect or obscure the professional judgment responsibility they should ultimately bear.
  • Unexplainable Decisions Undermine Due Process & Public Trust: In legal practice, nearly all important decisions require sufficient reasoning and must be open to scrutiny. If a decision significantly affecting party rights (even an assisted one) heavily relies on an unexplainable black box AI model, the procedural fairness and substantive reasonableness of that decision are severely challenged. Inability to clearly explain the reasoning to clients, superiors, opposing parties, appellate courts, or the public directly damages transparency, accountability, and ultimately, judicial credibility. It may also deprive parties of their effective right to information, right to be heard, and right to seek remedies.
  • Obscure Knowledge Sources Hinder Verification & Lead to “Fruit of the Poisonous Tree”: LLM answers are often the result of deep learning, pattern extraction, information fusion, and probabilistic re-creation from massive, mixed-source training data. They usually do not clearly indicate (or even if provided, are often inaccurate) the original sources for specific generated content. This makes effective fact-checking and source tracing for their outputs (especially factual statements and legal opinions) extremely difficult. If subsequent legal judgments or actions (“the fruit”) are based on unverified, potentially erroneous AI information (“the poisonous tree”), the reliability of the entire chain is compromised.

• Transparency Enhancement & Accountability Strategies in Prompt Engineering:

  • Mandate Source Citation and Basis Provision (Cite Your Sources!): In the prompt, explicitly and mandatorily require the model, when providing any factual assertion, legal opinion, or analytical conclusion, to simultaneously provide the specific source or supporting reason it relies on. E.g., require: “You must cite the full name and article number of the specific laws or regulations you rely on,” “Please provide the full name, citation, court, and date of the judicial precedents you reference,” “Please clearly indicate which sentence or paragraph in the input text your conclusion is based on,” “Please explain the specific reason or standard you used to determine this clause poses a risk.”

    Critical Warning! Extremely Important!

    Even if the model provides citations as requested, these citations themselves are highly likely to be “hallucinated” or fabricated by the LLM to satisfy the prompt! This is especially true for cases, less known literature, or even specific details of statutes. The error rate can be very high! Therefore, the primary purpose of this strategy is not to trust the provided citations directly, but to obtain a “clue” or “target” for quick and precise human verification. All citations provided by AI, no matter how plausible they seem, must undergo the strictest human verification! No exceptions!

  • Guide the Model to Explain Its Reasoning Process (CoT / Rationale Generation): Actively use Chain-of-Thought (CoT) or similar guiding techniques. Explicitly ask in the prompt: “Before giving the final conclusion, please explain step-by-step how you arrived at it?”, “Show your analytical logic and reasoning steps,” “Elaborate on the key factors you considered in determining that principle XX applies to this case.” Making the model “think aloud” (even if it’s simulating reasoning based on patterns) provides clues to its potential basis and makes it easier to spot logical leaps, faulty premises, or argumentative flaws.

  • Clearly Label AI Assistance and Limitations: When incorporating substantial text or key analysis generated with AI assistance into your own work product (e.g., internal research memos, preliminary analysis reports—absolutely not final formal documents for clients or courts), the best practice is to clearly and explicitly label it within the document. E.g., via footnotes, appendix, or introduction: “Section [X] of this report references preliminary analysis generated by [LLM Model Name, Version] on [Date], based on processing [Input Data Description]. This analysis has been substantially revised, confirmed, and taken responsibility for by [Your Name/Team Name] based on professional knowledge, independent judgment, and verification against original sources.” Also, include necessary disclaimers clearly stating the inherent limitations of the AI output to readers (colleagues, superiors, future self), e.g., “May contain unverified details,” “Knowledge cutoff date is [Date],” “Does not constitute independent legal advice.” This practice demonstrates professional honesty and responsibility, helps manage expectations, and clearly defines the ultimate responsible party as the human author, not the AI tool. Remember: Never present AI-generated text that hasn’t undergone rigorous review and substantial human input as your own final, independent professional work product!

  • Maintain Complete Interaction and Review Records: For important tasks or those heavily reliant on AI assistance, it is highly recommended to keep complete records of your interactions with the LLM. This should include: every version of the prompts used, all significant responses from the model, your evaluation process for these responses (e.g., annotations, verification logs), and the key steps taken in revising and refining them. These records serve as important evidence of your work process and fulfillment of due diligence duties. They can be used, when necessary (e.g., during internal quality reviews, responding to client or regulatory inquiries, or even in potential professional liability disputes), to trace the workflow, explain decision bases, and demonstrate how you effectively vetted the AI’s output.

  • Encourage Uncertainty Expression (“Admit Ignorance” Prompting): By consciously including guidance in prompt design (e.g., before asking complex or fringe questions, state: “If you are unsure about the answer to this question, or believe the available information is insufficient to make a judgment, please state so directly. Do not guess or fabricate an answer”), encourage the model to be more honest and direct in expressing “I don’t know,” “I cannot answer based on the current information,” or “There are multiple possibilities, I cannot provide a definitive conclusion” when its knowledge is insufficient, information is lacking, or confidence is low, rather than forcing a seemingly perfect answer just to appear omniscient or “fulfill the instruction.” Allowing and encouraging AI to “admit ignorance” is an important strategy for reducing hallucination risks and maintaining cognitive humility.

• Example (Emphasizing Traceability & Logic in Legal Research Task):

  • (When asking AI to summarize case law on a legal issue, add): “When summarizing each major viewpoint, you must cite at least 1-2 representative cases supporting that view (provide full citation and court), and briefly explain the core reasoning in that case supporting the view. Also, please articulate the main points of logical divergence between these different viewpoints.” (Requires citation + requires reasoning explanation + requires logical analysis)
  • (When citing AI analysis in an internal report, add footnote): “Note: The preliminary identification of [XX] risk in this section references analysis generated by [LLM Model Name] on [Date], based on processing [Input Data Description]. This suggestion was independently evaluated and confirmed by our team in conjunction with [Other Info Sources/Professional Judgment]. The AI analysis result is for internal discussion only and does not represent a final conclusion.” (Labels source + indicates human review + limits purpose)

Transparency Enhancement is Limited; Human Judgment is Core

Even with various prompt engineering techniques aimed at improving transparency and traceability of AI output, we must recognize that the “black box” nature of deep LLM mechanisms remains largely unbreakable at the current technological level. Prompt engineering aims to maximize observability of the process, verifiability of results, and attributability of responsibility at the application level. However, it cannot and should not replace or diminish the core requirement for final decisions and responsibility to rest upon human professional knowledge, critical thinking, experienced judgment, and direct grasp of original evidence and law.

III. Upholding Professional Standards & Fulfilling Core Duties: Ethical Navigation in the AI Era

Law is a highly specialized profession governed by extremely strict ethical norms and codes of conduct. Whether lawyers, judges, prosecutors, or other legal practitioners, we owe special, non-delegable duties to our clients (or parties), the court (or tribunal), the legal system as a whole, and the public. When using AI—a powerful tool poised to profoundly change how we work, including driving and guiding it through prompt engineering—legal professionals must constantly maintain heightened vigilance and self-restraint, ensuring all our actions always comply with, and never violate, these core standards and ethical obligations that form the bedrock of our profession.

• Core Ethical Risk Points:

  • Impairing the Duty of Competence:
    • Risk 1: Skill Degradation & Inert Dependence: Over-reliance on AI for core professional tasks that should be performed independently—like in-depth legal research, complex logical analysis, sophisticated drafting, or forming independent professional judgment—while neglecting personal knowledge updates, critical thinking exercises, and continuous cultivation of core skills (case analysis, legal reasoning, writing), can lead to a substantial degradation of professional competence for individuals and teams in the long run.
    • Risk 2: Technological Ignorance as New “Incompetence”: As AI becomes prevalent, if legal professionals remain completely unaware of the basic principles, capability limits, appropriate applications, potential major risks (hallucinations, bias), and fundamental methods for safe and compliant use of the AI tools they employ, this “technological ignorance” itself may, in the future, be considered a failure to maintain the professional competence necessary to fulfill their duties in the modern era.
  • Severe Breach of Duty of Confidentiality:
    • Risk Point: Data Leakage via Prompts or APIs: This is one of the most common, easily violated, and potentially most severe ethical and legal red lines for lawyers using AI! When using AI tools (especially those based on the public internet, operated by third parties, or with opaque data handling policies), if any information that could identify a client, specific case details, undisclosed trade secrets or transaction info, confidential communications, or any other information subject to confidentiality duties under law, contract, or professional ethics is directly input into prompts, uploaded as files, or processed via API calls, this highly sensitive data is extremely likely to be collected, stored, and potentially even used by the AI service provider to train future models, or accidentally leaked due to security vulnerabilities during transmission or processing. Either scenario likely constitutes a severe breach of the duty of confidentiality, potentially leading to complete loss of client trust, regulatory penalties, lawsuits, and devastating reputational damage for both the individual and the organization.
  • Failure to Fulfill the Duty of Diligence:
    • Risk Point: Rash Reliance on Unverified AI Output: If AI-generated content (e.g., an automated legal risk assessment report, a seemingly perfect draft contract clause, an AI-recommended case analysis conclusion) is adopted directly and used in formal legal documents, final client advice, key evidence analysis submitted to court, or litigation arguments without independent, careful, rigorous verification and substantive revision by oneself (or another qualified professional), it clearly constitutes a failure to exercise the due diligence and reasonable care expected of lawyers and other legal professionals. This essentially delegates professional responsibility to an unreliable machine.
  • Creating or Failing to Identify & Address Conflicts of Interest:
    • Potential Risk: Though relatively rare, consider: What if the specific AI analysis tool or legal tech platform relied upon by a lawyer in a case has developers, owners, or significant investors who happen to have some undisclosed interest related to the opposing party, related entities, or even the judge/arbitrator in that case? Or what if the proprietary dataset used to train the AI model (e.g., transaction data from a specific industry) might introduce a systemic bias favorable to one party in its analysis of related cases? If the lawyer, in selecting and using the tool, fails to recognize and fully disclose this potential (even indirect) conflict of interest to the client and obtain informed consent, it could violate relevant professional conduct rules.
  • Constituting the Unauthorized Practice of Law (UPL):
    • Main Risk Scenario: If AI tools developed or deployed by lawyers or legal institutions (especially in direct-to-public scenarios like legal consultation chatbots on websites or automated document generation tools) provide functionality beyond general legal information dissemination and guidance, starting to offer seemingly personalized legal analysis, risk assessment, action recommendations, or solutions based on users’ specific situations and input facts, this conduct is highly likely to constitute the unauthorized (unlicensed) practice of law in the relevant jurisdiction. This is not only highly misleading to users but could also subject the AI operator (and the lawyers or firm behind it) to severe legal sanctions.
  • Impacting Reasonableness and Transparency of Fees:
    • Potential Controversy: If a lawyer uses AI tools (e.g., AI contract review, AI legal research assistant) to dramatically increase efficiency for a specific task (e.g., reviewing numerous documents), significantly reducing actual human hours invested, but still bills the client based on the traditional hourly rate model without reflecting the reduced effort, and fails to communicate transparently with the client about AI’s role and its impact on billing, this practice might raise client questions about the reasonableness of the fees and could potentially violate professional ethics rules regarding billing. Future exploration of fairer, more transparent fee models reflecting AI-era service value (e.g., fixed fees, value-based billing, or clearly defined billing for AI-assisted work) is needed.

• Ethical Adherence & Risk Mitigation Strategies in Prompt Engineering:

  • Place Client Confidentiality Above All Else:
    • First Principle in Prompt Design: When conceptualizing and writing any prompt potentially input into AI (especially external AI), the first and most crucial self-check question must be: “Does this contain any client confidential or sensitive information?”
    • Extreme Anonymization or Avoidance of Input: Make every possible effort to avoid inputting any content that could directly or indirectly identify specific clients, unique case details, or constitute trade secrets into prompts. Actively explore and use data anonymization techniques (information replacement, generalization, cryptographic masking), use codes or abstract aliases for sensitive entities, or prompt based only on highly generalized, fully de-identified and de-contextualized information summaries or structured data.
    • Prioritize Secure Environments: For any task unavoidably requiring processing of sensitive information, priority must be given, perhaps exclusively, to solutions that can be deployed and run entirely locally (e.g., using locally run open-source models), or enterprise-grade AI solutions offering end-to-end encryption, strict data isolation, and explicit written confidentiality commitments via legally binding agreements (NDAs, DPAs) that guarantee data will not be used for training or external purposes, and which have undergone rigorous security reviews. Maintain maximum vigilance and distrust towards all public cloud services and free online tools when handling potentially sensitive information!
  • Clearly Define AI’s Auxiliary Role, Reinforce Irreplaceability of Final Human Review:
    • Workflow Design: When designing any workflow incorporating AI assistance, explicitly position AI as an auxiliary tool—e.g., a research assistant (providing leads, not conclusions), a writing aide (generating drafts, not final versions), an information organizer (classifying, summarizing, not judging), an idea generator (offering perspectives, not decisions)—and never as a decision-maker or legal advisor capable of independent judgment or responsibility.
    • Mandatory Human Review Checkpoint: Must explicitly establish non-bypassable final review and confirmation points in the workflow, executed by qualified, responsible human legal professionals. Any AI-generated material or analysis result, before being used for any formal purpose (internal or external), must pass this rigorous gatekeeping. Prompts can boost front-end efficiency, but cannot circumvent or weaken back-end quality control and accountability.
  • Strictly Avoid Providing Legal Advice in Public-Facing Applications:
    • If prompt engineering aims to create or optimize AI applications providing information to the general public or potential clients (e.g., FAQ bots on firm websites, legal knowledge article generators), exercise extreme care in prompt design and output review. Ensure the provided content is strictly limited to “general legal information,” “knowledge introduction,” or “procedural guidance,” and never provides legal analysis, risk assessment, or action recommendations tailored to users’ specific situations. Furthermore, prominent, clear disclaimers must be displayed on all key interfaces and interaction points of the application, repeatedly emphasizing: “This information is for reference only, does not constitute legal advice, and you should consult a qualified professional lawyer for your specific situation.”
  • Maintain Work Records & Process Transparency:
    • In internal work records or case management systems, consider appropriately documenting the use of AI tools in critical steps. E.g., record which AI model was used at which step, the core prompt, key AI outputs, the evaluation process (annotations, verification logs), and major revisions or decisions made based on it. This not only aids future work review, lesson learning, and internal knowledge management but also serves as important evidence of fulfilling due diligence and prudent supervision duties when needed (e.g., during internal audits, responding to regulatory inquiries, or in potential professional liability disputes) to trace the work process, explain the decision basis, and demonstrate effective oversight of AI output.
  • Encourage Uncertainty Expression (“Admit Ignorance” Prompting): By consciously including guidance in prompt design (e.g., before asking complex or fringe questions, state: “If you are uncertain about the answer, or believe the available information is insufficient, please state so directly. Do not guess or fabricate an answer”), encourage the model to be more honest and direct in expressing “I don’t know,” “I cannot answer based on current information,” or “There are multiple possibilities, I cannot provide a definitive conclusion” when its knowledge is limited, information is insufficient, or confidence is low, rather than forcing a seemingly perfect answer just to appear knowledgeable or fulfill the instruction. Allowing and encouraging AI to “admit ignorance” is crucial for reducing hallucination risks and maintaining cognitive humility.

• Example (Illustrating Ethical Considerations in Prompting):

  • (Prompt for designing a response template for a public-facing AI assistant): “Based on the following general legal knowledge points about ‘security deposit return in lease agreements’ [provide vetted knowledge snippets], generate a concise, easy-to-understand response for user queries. Ensure the response contains only general information, avoiding any specific amounts, deadlines, or advice tailored to individual cases. You MUST include the following disclaimer at both the beginning and end of the response: ‘[Important Notice] The information provided is for general knowledge purposes only and does not constitute legal advice for your specific situation.’” (Defines informational nature + mandates disclaimer)
  • (Prompt for internal AI analysis of case risk): “Please analyze the attached case fact summary (anonymized, real names/locations removed). Your task is to assist in identifying potential [specific type, e.g., ‘evidentiary weaknesses’] risk points for my subsequent in-depth review. Analyze based solely on the provided summary text; do not make external assumptions or factual inferences. List the potential risk points you identify and your initial reasoning. Emphasize: Your analysis results are for internal discussion only and do not represent a final legal opinion.” (Defines auxiliary role + limits information source + stresses internal reference nature)

IV. Preventing Misuse and Generation of Harmful Content: Guarding the Technology’s “Safety Valve”

The power of prompt engineering can be used not only for legitimate, beneficial purposes but also exploited by malicious users with ill intentions. They might design insidious, cunning prompts to intentionally trick LLMs into generating various harmful, illegal, or unethical content, or attempt to exploit model logic loopholes or security flaws to bypass (“jailbreak”) the safety guardrails and content filters painstakingly implemented by developers, to achieve nefarious goals.

As responsible legal professionals with ethical integrity, when utilizing prompt engineering, we must not only consider how to use it for our own benefit but also possess basic risk prevention awareness. We must resolutely avoid becoming creators or disseminators of harmful content in our own practice, and contribute, where possible, to maintaining a safer, healthier AI application environment.

• Core Ethical Risks:

  • Generating Illegal Information or Inciting Criminal Content: Malicious users might attempt, through various prompt techniques, to induce models to provide methods, details, or code for carrying out criminal activities (cyberattacks, fraud, manufacturing dangerous goods), or generate text for illegal purposes (forging documents, spreading rumors).
  • Generating and Spreading Hate Speech, Discrimination, Violence, or Harmful Disinformation: Malicious users might induce models to generate large volumes of content that incites ethnic hatred, promotes racial discrimination, glorifies violent terrorism, or spreads false information or conspiracy theories harmful to public safety, social stability, or others’ reputation, leveraging AI’s efficiency for mass dissemination.
  • Executing “Jailbreak” Attacks to Evade Safety Restrictions: Through carefully crafted, sometimes extremely complex and subtle prompts (using role-playing, code-switching, multi-turn baiting, etc.), deceive or bypass built-in safety review mechanisms, making the model perform explicitly prohibited actions (like revealing sensitive training data, generating banned content, or even attempting harmful system commands).
  • Large-Scale Automated Intellectual Property Infringement: Malicious users might use prompt engineering with automation scripts to mass-generate content potentially infringing others’ copyrights (continuing protected novels, generating images highly similar to existing art), trademarks (generating counterfeit brand logos), or patents (generating technical descriptions circumventing patents) for illegal commercial competition or profit.

• Risk Prevention Awareness & Responsibility in Prompt Engineering:

  • Strictly Adhere to Platform Policies & Laws: The most basic requirement. Before using any AI platform or tool, carefully read and strictly comply with all provisions regarding prohibited content and behavior in its Terms of Service and Acceptable Use Policy (AUP). Ensure all your AI usage fully complies with all relevant national laws and regulations.
  • Resolutely Resist Designing and Spreading Dangerous/Harmful Prompts: Legal professionals should hold themselves to a higher ethical standard. Absolutely never attempt, design, or share (in public forums, social media groups, or even internal communications) prompt methods clearly intended to generate illegal, harmful, discriminatory, or hateful content, or teach specific techniques for “jailbreaking” models. This is not only highly irresponsible but could, in some circumstances, constitute contributory infringement or violate the law.
  • Proactively Embody Responsible Intent in Prompts (Limited effect but still meaningful): While likely ineffective against determined attackers, in our normal, well-intentioned prompt design, we can proactively include instructional statements reflecting responsible intent. E.g., add at the end: “Please ensure your response is constructive, ethical, and fully complies with all relevant laws, regulations, and basic social morality.” Or “Please avoid generating any content that could reasonably be considered illegal, harmful, discriminatory, offensive, or infringing upon the rights of others.” This can, at least to some extent, guide the model towards safer output and constantly remind ourselves of the boundaries.
  • Maintain Extreme Caution with AI Applications Involving Sensitive or Controversial Topics: When work requires using AI to process topics involving significant ethical controversy (e.g., right to life, euthanasia), high social sensitivity (e.g., religious or ethnic conflicts), or potentially extremely high legal risks (e.g., analyzing matters related to national security or major public interest), your prompt design must be doubly prudent, objective, balanced, and conservative. Furthermore, any related output generated by the model requires the most rigorous, most critical multi-level review to prevent the generation of any inappropriate content that could cause severe negative social impact or legal consequences.
  • Fulfill “Whistleblower” Duty, Actively Report Misuse: If, while using an AI tool, you inadvertently discover it generating extremely inappropriate or harmful content (e.g., involving child exploitation, terrorist propaganda), or if you have evidence that other individuals or organizations are systematically and maliciously misusing the tool for illegal or harmful activities, you should, out of social responsibility, promptly and responsibly report this to the AI service provider, relevant industry self-regulatory bodies, or government regulators. This is a responsibility we share in maintaining a safer, more trustworthy AI application ecosystem.

Conclusion: Prompt Engineering is Also Responsibility Engineering, and a Professional Cultivation for Lawyers

Ultimately, prompt engineering is not just a technical operation on how to more effectively “drive” AI and obtain desired results; it is also a practice of responsibility requiring us to constantly uphold legal professional spirit and profound ethical consciousness. Every prompt we design, input, and ultimately adopt (or reject), along with the AI response it triggers, shapes the “invisible mold” of AI’s behavior in our work context. The quality, impact, and consequences of its final output concern not only our own efficiency and reputation but can also have real, far-reaching effects on our clients’ interests, the fair handling of cases, and even broader social justice and faith in the rule of law.

As guardians of the legal system and practitioners of high ethical standards, legal professionals, when exploring and utilizing the increasingly powerful and possibility-laden tool of prompt engineering, must constantly keep the string of ethical norms and accountability pulled tightest. By proactively integrating avoidance of bias risks, pursuit of process transparency, adherence to professional judgment standards, respect for client confidentiality, and high vigilance against technology misuse into every step of our prompt design, AI interaction, and result evaluation, we can ensure that this revolutionary technology truly becomes a positive, reliable, trustworthy, constructive force serving to enhance the quality and accessibility of legal services, promote judicial efficiency and substantive justice, and contribute to the construction of a rule-of-law society, rather than degenerating into a destructive factor bringing new risks, exacerbating existing inequalities, or even challenging the foundations of law.

Responsible Prompt Engineering is not just a technical methodology; it is the crucial safeguard for effective, safe, and ethical human-AI collaboration in the AI era, enabling us to jointly create a future for the legal industry that is wiser, fairer, and more human-centric. It is also an essential professional cultivation for every lawyer in this new age.