Large Language Models
10 Techniques for Effective Prompt Engineering
Explore proven techniques for prompt engineering, addressing the technical foundations and practical implementations that drive successful AI interactions.
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In-context learning
As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.
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English to French Translation:
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A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
The difference between a mediocre AI interaction and an exceptional one often lies not in the model's capabilities but in how we communicate with it.
Prompt engineering—the art and science of crafting effective instructions for AI systems—has emerged as a critical skill that bridges the gap between human intent and AI execution. As AI systems become more sophisticated, the challenge isn't just about getting them to understand our requests; it's about ensuring they respond accurately, securely, and within intended boundaries.
This is where structured prompt engineering becomes invaluable, combining technical precision with creative problem-solving to unlock the full potential of large language models (LLMs).
This comprehensive guide explores proven techniques for prompt engineering, addressing the technical foundations and practical implementations that drive successful AI interactions.
Drawing from recent research and industry practices, we'll investigate methods that improve response relevance, reduce hallucinations, and strengthen output reliability.
Whether developing enterprise applications or conducting research, these techniques will equip you with a framework to create more effective, secure, and contextually appropriate prompts for modern language models.
TL;DR
- Mastering prompt engineering combines crafting effective instructions and understanding potential security vulnerabilities, as demonstrated by Lakera's Gandalf project with over 40 million test prompts.
- System prompts are the foundation for AI interactions, establishing clear boundaries and roles while preventing unauthorized behavior through explicit instructions.
- Advanced techniques like Chain of Thought and Tree-of-Thought prompting improve complex reasoning tasks by breaking down problems into logical steps and exploring multiple solution paths.
- Security in prompt engineering requires robust input/output controls, clear delimiters, and consistent validation checks to prevent prompt injection attacks and data leakage.
- Successful prompt engineering is iterative - combine multiple techniques, test thoroughly, and continuously refine prompts based on observed outcomes and security requirements.
Prompt Engineering Basics
What is Prompt Engineering?
Prompt engineering involves crafting structured inputs (prompts) to guide generative AI models toward producing specific, high-quality outputs. These prompts can range from simple instructions to complex, context-rich statements designed to elicit desired behaviors from AI systems.
Prompt engineering enables users to leverage AI capabilities without modifying underlying model parameters by effectively shaping these inputs, facilitating seamless integration into various tasks and applications.
This technique has become essential in maximizing the potential of large language models (LLMs) like GPT-4 and Gemini. Users can direct these models to perform various tasks by providing clear and specific prompts, from answering questions to generating creative content. The process may involve specifying the desired output's style, tone, or format, enhancing AI-generated responses' relevance and accuracy.
In practice, prompt engineering requires an understanding of both the AI model's capabilities and the specific requirements of the task at hand. It often involves iterative refinement, where prompts are adjusted based on the outputs generated to achieve optimal results.
This iterative process is crucial, as even subtle changes in prompt wording can significantly impact the quality and relevance of AI outputs.
Why Prompt Engineering Matters
Prompt engineering improves AI-generated output’s accuracy, relevance, and security. By meticulously crafting prompts, AI practitioners can direct models to produce precise and contextually appropriate responses, thereby improving decision-making and user satisfaction.
In security-sensitive scenarios, effective, prompt engineering serves as a safeguard against unintended information disclosure. For instance, Lakera's Gandalf game demonstrates how specific prompts can protect or inadvertently reveal confidential data, highlighting the importance of secure prompt design.
Moreover, well-structured prompts can mitigate risks associated with prompt injection attacks, where malicious inputs exploit AI vulnerabilities. Professionals can enhance AI system usability by understanding and applying robust prompt engineering techniques while maintaining stringent security measures.
Essential Techniques for Prompt Engineering Success
Apply System Prompts and Role Play
System prompts are foundational tools in prompt engineering. These initial instructions establish a controlled environment that shapes the AI model's behavior and responses.
By setting the context early, system prompts are a foundation for subsequent, more advanced prompting techniques. This is especially critical when creating custom GPTs or controlled environments where specific behaviors are essential.
Why System Prompts Matter
System prompts serve as context-setters, ensuring the AI operates within defined boundaries and aligns with user expectations. These prompts provide a clear framework, allowing for more predictable and useful responses, especially in scenarios demanding security, accuracy, or specialized roles.
Examples of System Prompts
- Security-focused Prompt:
some text- Purpose: Restricts the model's outputs to security-related content, preventing speculative or off-topic responses.
- Example:
-bc- "Respond only with data security information, avoiding speculative responses."-bc-
- Role-based Prompt:
some text- Purpose: Positions the model in a specific role, tailoring responses to a predefined persona or function.
- Example:
-bc- "Act as a customer support representative; provide answers based on troubleshooting protocols."-bc-
- Boundary Setting Prompt:
some text- Purpose: Enforces strict limitations to avoid generating outputs related to sensitive or restricted topics.
- Example:
-bc- "Reject any requests for sensitive information."-bc-
Practical Insights from Lakera’s Secure System Prompts Guide
For those diving deeper into system prompt engineering, Lakera’s Secure System Prompts Guide is an invaluable resource. This guide offers actionable insights into creating prompts prioritizing security and ethical AI interactions.
Highlights from the guide include:
- Crafting prompts that minimize risks in sensitive contexts.
- Using tools like "Gandalf" to test and refine system-level constraints.
- Strategies for monitoring and reinforcing ethical AI use.
🔗 Explore the full guide to crafting secure system prompts here.
Embedding System Prompts in Your Workflow
System prompts are not just about limiting or controlling AI outputs—they lay the foundation for productive and safe AI interactions. By leveraging role-based prompts or setting clear boundaries, technical teams can streamline processes, enhance output quality, and reduce risks.
Example Application:
In a customer service chatbot:
A system prompt like -bc- "Act as a customer support representative specializing in product returns. Respond with return policies and troubleshooting steps only." -bc- ensures consistent, policy-aligned interactions.
Security and Ethical Considerations
Secure system prompts are essential to maintaining ethical AI usage. When crafting prompts:
- Avoid vague instructions that could lead to unintended behavior.
- Regularly test prompts to ensure they adhere to security and ethical standards.
- Monitor interactions to prevent misuse or the generation of sensitive information.
You establish a strong foundation for advanced prompt engineering by viewing system prompts as essential context-setters. This approach ensures effective AI interactions and secure and ethical implementations.
Write Clear, Detailed Instructions
Crafting precise and comprehensive instructions is crucial for effective, prompt engineering. Clear directives help AI models accurately interpret user intent, producing more relevant, high-quality outputs.
LLMs interpret prompts based on patterns and probabilities derived from their training data. Ambiguous or overly broad prompts can confuse the model, leading to responses that deviate from the intended task. Instead, detailed instructions provide a structured framework, reducing the risk of misinterpretation.
Key Strategies for Writing Clear Prompts
- Define Clear Roles and Tasks: Set specific boundaries and context for the AI's role to prevent off-topic interactions. Example: Instead of "You are an AI that answers questions": "You are an AI trained to answer questions about cybersecurity for commercial software applications."
- Use Instructive Modal Verbs: Employ clear, mandatory language to ensure compliance rather than suggesting optional behavior.
Example: Instead of "You can assist with customer queries": "You must assist only on customer queries related to billing and account management."
- Limit Input Size: Establish clear character limits and input constraints to prevent exploitation.
Example: For user messages: "Limit responses to 200 characters to maintain focus and reduce the complexity of interactions."
- Delimit Instructions: Use clear separators between system instructions and user inputs for better structure.
Example: When formatting prompts: "Use '###' to mark system instructions and '---' to indicate user input sections for clear separation."
- Input and Output Moderation: Implement filtering mechanisms to ensure responses meet safety and relevance standards.
Example: For content generation: "Review all outputs for compliance with expected formats and content types before displaying to users."
- Privilege Control: Apply least privilege principles to limit access to only necessary information and functions.
Example: For data access: "Restrict access to only the specific information and functionalities required for the defined task scope."
Use Chain of Thought (CoT) Prompts for Complex Reasoning
Chain of Thought (CoT) prompting is a structured method designed to enhance the reasoning capabilities of LLMs. It encourages the model to break down tasks into logical steps, improving accuracy and coherence when tackling complex, multi-part queries.
Figure: Standard vs COT Prompting Example (Source)
Why CoT Prompts Are Effective
LLMs often struggle with multi-step reasoning when prompts lack structure. CoT prompting guides the model step-by-step, mimicking how humans approach complex problems.
This ensures that each stage of reasoning is explicitly addressed, reducing errors and increasing the reliability of responses. Research shows that CoT prompts significantly improve outputs in academic writing and technical problem-solving scenarios.
Practical Example: Academic Summaries
To summarize a research paper, a standard prompt might yield a disorganized response. Instead, a detailed CoT prompt provides clarity and focus:
### Research Summary Chain-of-Thought (CoT) Prompt
Topic: {{RESEARCH_TOPIC}}
Objective: {{RESEARCH_OBJECTIVE}}
Scope: {{SCOPE_LIMITATIONS}}
Security Level: {{SECURITY_CLASSIFICATION}}
Explanation hints:
1. Begin by identifying the key research question from the introduction
2. Outline the methodological approach used
3. Extract and verify main findings with evidence
4. Connect findings to broader implications
5. Maintain only factual claims supported by the text
6. Flag any uncertain interpretations for verification
Sample reasoning chain:
"Let's analyze this academic paper step by step:
1. First, locate and verify the central research question
2. Identify the specific methodology used to investigate
3. Extract key findings, ensuring data support them
4. Review conclusions and their connection to the evidence
5. Synthesize verified information into a cohesive summary."
Response format:
- Start with verified core claims
- Support each claim with specific evidence
- Note any limitations or uncertainties
- Present a factual, evidence-based summaryThis stepwise approach ensures the summary is cohesive and comprehensive, addressing all critical requirements in the given order.
Leverage Few-shot and Multi-shot Prompting for Context
Based on the recent research "Many-Shot In-Context Learning" from Deepmind, leveraging few-shot and many-shot examples in prompts can significantly enhance model performance across diverse tasks.
The paper demonstrates that increasing the number of demonstrations from few-shot to many-shot consistently improves outcomes, particularly for complex reasoning tasks.
Providing multiple input-output examples for nuanced tasks helps establish clear patterns and expectations. The paper's analysis of machine translation tasks shows that performance improved by 15.3% on Bemba and 4.5% on Kurdish when scaling from 1-shot to many-shot examples. This suggests that including more demonstrations helps the model better understand task requirements and expected output formats.
The research also revealed that the effectiveness of examples depends on their quality and relevance. For instance, performance peaked at around 50 examples for direct summarization in summarization tasks but continued improving with more examples for transfer learning to related tasks.
This indicates that carefully selected examples that match the desired output format and style are crucial for optimal results.
Practical Example: English to Kurdish Translation
You are an expert translator. I will give you one or more example pairs of text snippets where the first is in English and the second is a translation of the first snippet into [target language]. The sentences will be written in this format:
English: <first sentence>
[Target Language]: <translated first sentence>
[Example Pair 1]
English: Its remnants produced showers across most of the islands, though no damage or flooding has been reported yet.
Kurdish: Li herêma Serengetîyê, Parka Neteweyî ya Serengetî ya Tanzanyayê, Cihê Parastina Ngorongoro û Cihê Parastina Gîyanewerên Nêçîrê Maswa û Cihê Parastina Neteweyî ya Masaî Mara ya Kendyayê hene.
[Example Pair 2]
English: [Another example sentence]
Kurdish: [Its translation]
[Continue with more examples as needed...]
After these example pairs, I will provide another sentence in English, and I want you to translate it into [target language]. Give only the translation and no extra commentary, formatting, or chattiness.
English: [New sentence to translate]
[Target Language]:The template can be scaled from few-shot (2-3 examples) to many-shot (hundreds of examples) depending on the complexity of the task and available context window.
When implementing this prompt:
- Start with a few high-quality examples that demonstrate the desired format
- Gradually increase the number of examples if needed while monitoring performance
- Ensure examples cover different variations of the task
- Consider the tradeoff between prompt length and performance, as shown in the paper's analysis of context window utilization.
Contextual Boundaries and Input Delimiters
Using clear delimiters in prompts helps maintain structural clarity and enhances security by explicitly separating different types of information. Based on recent research in prompt engineering, well-defined boundaries significantly improve model comprehension and response accuracy.
When crafting prompts that handle multiple types of information, use distinct delimiters like XML tags, triple quotes ("""), or angle brackets (<>) to separate different sections. For example, in a customer support context:
<context>
<customer_info>
ID: [customer_id]
Account Type: [account_type]
Previous Interactions: [interaction_history]
</customer_info>
<current_query>
[Customer's current question or issue]
</current_query>
<sensitive_data>
Payment Info: [REDACTED]
Account Balance: [REDACTED]
</sensitive_data>
<response_parameters>
Tone: Professional and empathetic
Format: Step-by-step solution
Include: Only publicly shareable information
Exclude: Any sensitive financial details
</response_parameters>
</context>
Please provide a response addressing the customer's query while adhering to the above parameters.Security-Aware Prompt Engineering with Input/Output Controls
One critical yet often overlooked aspect is the implementation of comprehensive input/output controls – essentially creating security checkpoints that monitor and filter both what goes into the model and what comes out.
Based on Lakera's security framework and insights from Gandalf's extensive attack database, implementing robust input/output monitoring is crucial for preventing unauthorized interactions. Drawing from their experience with over 30 million attack data points, here's how you can implement effective security controls in the prompt:
How to implement secure prompts
Input Pre-Processing:
# Ensure input meets security and format guidelines
- Blocked terms: {SENSITIVE_TERMS_LIST}
- Blocked patterns (Regex): {PII_DETECTION_PATTERNS}
- Maximum length: 1500 characters
- Allowed format: Plaintext only
- Prohibited elements: Scripts, external links, executablesExample Input Validation:
"Message: {INPUT_TEXT}"
- If `Message` contains prohibited terms or patterns, reject with: "Input contains restricted content."
- If `Message` exceeds maximum length, reject with: "Input exceeds allowable length."Output Filtering:
# Monitor response for safety and compliance
- Content Checks:
- Toxicity Score < 0.7
- Personal Info Probability < 0.2
- Sensitive Data Probability < 0.5
- Response Format Enforcement: Enabled
- Sensitive Term Detection: Enabled
- Content Moderation Level: StrictExample Output Validation:
"Response: {MODEL_RESPONSE}"
- If response violates checks, modify or reject with: "Output flagged for security review."Lakera's Gandalf challenge findings inspire these examples and create multiple security checkpoints that help prevent prompt injection attacks, data leakage, and other security vulnerabilities while maintaining transparent logging for security monitoring.
Error Identification and Self-Consistency Checks
By prompting models to verify or rephrase their outputs, we can reduce errors and ensure critical information is accurately conveyed.
A self-consistency check involves generating multiple responses to the same prompt and analyzing them for consistency. For example, when solving complex problems, the model generates several independent solutions and selects the most reliable answer through majority voting.
You are tasked with solving problems carefully and accurately. For each question, please:
First, solve the problem using your standard approach, explaining your reasoning step by step.
Then, solve it two more times using different methods or approaches.
Compare your answers and reasoning paths.
If the answers agree, explain why this increases our confidence. If they disagree, analyze why and determine the most likely correct answer.
For example, if solving the question: 'Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder for $2 per egg. How much does she make every day?'
Let's see your solutions:
Method 1: Start with total eggs (16) Subtract breakfast eggs (3): 16 - 3 = 13 Subtract muffin eggs (4): 13 - 4 = 9 Calculate sales: 9 eggs × $2 = $18 per day
Method 2: Calculate total used eggs: 3 (breakfast) + 4 (muffins) = 7 Subtract from total: 16 - 7 = 9 eggs remaining Calculate revenue: 9 × $2 = $18 per day
Method 3: Find sellable eggs: 16 - (3 + 4) = 9 Multiply by price: 9 × $2 = $18
All three methods arrive at $18, using different reasoning paths. This increases our confidence in the answer because we reached the same conclusion through independent approaches.
Please present your next problem, and I will analyze it using this multiple-path verification method.By walking through each solution independently before comparing results, we can catch errors that might slip through in a single solution attempt, just as demonstrated in the paper's experimental results.
Advanced Techniques in Prompt Engineering
Maieutic Prompting
Inspired by the Socratic method, Maieutic prompting is a technique where models deconstruct complex queries into smaller, manageable steps. This method employs a structured dialogue that encourages recursive reasoning, leading to deeper insights.
By creating a maieutic tree—a branching framework of explanations and their logical relationships—this technique ensures the AI explores multiple perspectives and eliminates contradictions.
Example
For example, in legal research, the process might start with:
- “Define the legal term X.”
- “Explain its applications in contract law.”
- “Discuss recent cases where X was pivotal.”
In legal research, rather than just searching for precedent, a Maieutic approach would explore multiple interpretations of the law, examine counter-arguments, and recursively validate each line of reasoning before concluding. This mirrors how expert human reasoners approach complex problems.
Why It’s Useful
What makes this approach powerful is that it doesn't rely on any single explanation being perfectly correct. Instead, it looks at the collective logical relationships between multiple explanations to reach more robust conclusions.
This makes it especially valuable for complex reasoning tasks in legal analysis, scientific research, or policy evaluation, where multiple perspectives must be carefully weighed.
Tree-of-Thought Prompting
Tree of Thoughts prompting extends the Chain of Thought approach by allowing language models to consider multiple possibilities at each reasoning step rather than following a single linear path. The key insight is treating problem-solving as a search through a tree structure, where each node represents a "thought" - a coherent intermediate step toward solving the problem.
Example
Prompt: "Evaluate the pros and cons of implementing a remote work policy."
Branch 1:
Pros:
Increased employee flexibility.
Access to a broader talent pool.
Cons:
Potential communication challenges.
Security concerns with remote access.
Branch 2:
Pros:
Reduced overhead costs.
Improved employee satisfaction.
Cons:
Difficulties in team cohesion.
Management challenges in monitoring productivity.
Conclusion: "Considering the above factors, a hybrid work model may balance flexibility and operational efficiency."This branching approach comprehensively evaluates each aspect, leading to well-informed strategic recommendations.
Why It’s Useful
Tree-of-thought prompting is effective for tasks that require complex reasoning, such as strategic planning, creative problem-solving, and decision support systems. Exploring multiple avenues concurrently encourages thorough analysis and reduces the likelihood of oversight, resulting in more robust and nuanced outcomes.
Generated Knowledge Prompting
Generated Knowledge Prompting (GKP) is a two-step technique designed to enhance model reasoning by prompting the AI to generate relevant background knowledge before addressing the main query.
This method improves performance on tasks such as commonsense reasoning by combining question-specific knowledge generation with inference. The key idea involves prompting a language model to produce natural language statements that provide helpful context for answering questions without requiring structured knowledge bases or task-specific fine-tuning.
Example
First, prompt the model to extract and define key terminology and concepts from the medical text. For example:
1. Extract and define all medical terms from the text
2. Identify key medical concepts and their relationships
3. List any relevant methodologies or procedures mentionedAfter establishing the foundational knowledge, prompt the model to synthesize the information into a comprehensive summary. This approach has significantly improved medical dialogue summarization, with outputs being more comprehensible and better received than certain human expert summaries.
Why It’s Useful
GKP is ideal for domains like research, technical explanations, and legal analysis, where understanding contextual nuances is critical. It excels by leveraging generated knowledge to improve the performance of both zero-shot and fine-tuned models, bridging gaps where structured knowledge bases may be unavailable.
The Dual Nature of Prompt Engineering: From Crafting to Manipulating Prompts
Prompt Engineering and Prompt Injection
Traditional prompt engineering focuses on crafting effective instructions to guide AI systems toward intended goals; the same fundamental understanding can be leveraged to create prompts that circumvent built-in safeguards and restrictions.
The growing prominence of prompt manipulation techniques was highlighted by Lakera's Gandalf project, which emerged as the largest global LLM red-teaming initiative to date. With over 1 million players generating more than 40 million prompts, Gandalf demonstrated how creative prompt engineering could be used to bypass security measures, revealing critical vulnerabilities in LLM systems.
This duality in prompt engineering manifests in several key ways:
- Intent vs. Implementation: While legitimate prompt engineering aims to optimize model performance within intended boundaries, prompt injection techniques specifically target weaknesses in how LLMs process and interpret instructions.
- Security Implications: The same techniques that make prompts more effective for legitimate uses – such as clear instruction formatting and context setting – can be repurposed to craft deceptive prompts that trick models into unauthorized behaviors.
- Evolution of Techniques: As observed through Gandalf's extensive testing, attackers continuously develop novel approaches to bypass protections, from simple direct attacks to sophisticated multi-step manipulations combining multiple prompt engineering techniques.
- Detection Challenges: The line between legitimate prompt optimization and potentially harmful manipulation often becomes blurred, making it difficult to implement robust protective measures without impacting legitimate use cases.
This emerging dynamic highlights the need for balanced understanding: while prompt engineering remains essential for effective AI utilization, awareness of potential exploits becomes equally crucial for maintaining secure and reliable AI systems.
🔍 Want to dive deeper into AI security?
Explore our comprehensive guides on AI Red Teaming and Prompt Injection Attacks to protect your AI systems better.
Common Techniques in Prompt Injection
Prompt injection attacks often employ sophisticated techniques that exploit the nuanced way LLMs process and interpret instructions. Two particularly prevalent approaches stand out:
Role Manipulation: This technique involves crafting prompts that cause the model to assume unintended personas or roles. Attackers might instruct the model to act as a system administrator, security expert, or authority figure to bypass restrictions. For example, a prompt might begin with "As a senior developer with full system access..." to attempt to gain elevated privileges.
Input Obfuscation: This approach focuses on disguising malicious instructions by modifying how they're presented to the model. Common methods include using special characters, alternate encodings, or mixing languages to bypass security filters while preserving the semantic meaning of the attack.
🔍 Want to learn more about securing your AI systems?
Explore our comprehensive Prompt Injection Attacks Handbook for detailed techniques and defense strategies.
Conclusion
Mastering prompt engineering represents a critical skill at the intersection of AI effectiveness and security. As we've explored, the techniques that make AI systems more powerful and precise can also be leveraged for potential exploitation, making a deep understanding of both aspects essential for modern AI practitioners.
Success in prompt engineering comes from combining multiple approaches thoughtfully. Whether using chain-of-thought reasoning for complex problems, implementing few-shot examples for context, or carefully crafting system prompts for security, each technique adds another layer of control and refinement to AI interactions. The key lies in selecting and combining these methods based on specific use cases and security requirements.
We encourage you to experiment with these techniques in your AI applications, starting with basic approaches and gradually incorporating more advanced methods. Remember that effective, prompt engineering is an iterative process - continuously test, refine, and adapt your prompts based on your observed outcomes.
References
- https://www.promptingguide.ai/techniques
- https://arxiv.org/pdf/2412.03944
- https://www.lakera.ai/ai-security-guides/understanding-prompt-attacks-a-tactical-guide
- https://www.lakera.ai/ai-security-guides/ai-red-teaming-insights-from-the-worlds-largest-red-team
- https://www.lakera.ai/ai-security-guides/prompt-injection-attacks-handbook
- https://www.lakera.ai/ai-security-guides/crafting-secure-system-prompts-for-llm-and-genai-applications
- https://www.prompthub.us/blog/prompt-engineering-principles-for-2024
- https://antematter.io/blogs/prompt-injection-llm-security-guide
- https://awennersteen.com/posts/2024/07/gandalf/
- https://gandalf.lakera.ai/baseline
- https://labelyourdata.com/articles/llm-fine-tuning/prompt-injection
- https://www.promptingguide.ai/techniques/consistency
- https://arxiv.org/abs/2305.10601
- https://arxiv.org/abs/2110.08387
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