Navigating the World of LLMs: A Beginner’s Guide to Prompt Engineering-Part 2
From Basics To Advanced Techniques
Introduction:
Prompt engineering is the process of designing and creating effective instructions or questions (called “prompts”) that help LLMs (Large Language Models) understand what you want them to do and generate the desired response or output.
Think of it like giving clear and specific instructions to a helpful assistant. By carefully crafting these instructions, you can guide the LLM to provide more accurate, relevant, and useful responses.
It involves understanding how LLMs interpret and respond to prompts and then using that knowledge to create prompts that are easy for the LLM to understand and follow. The goal of prompt engineering is to improve the communication between humans and LLMs, leading to better results and experiences. In simpler terms, prompt engineering is about figuring out the best way to “talk” to LLMs to get the most helpful and accurate responses.
In this conversation, we will explore several influential research papers focused on prompt engineering. We’ll delve into the key concepts and ideas presented in these papers and provide concrete examples to illustrate how these techniques can be applied in practice.
Prompting Techniques for Large Language Models: A Practitioner’s Guide:
The paper is
Key Points:
1. The paper presents a comprehensive framework for categorizing the diverse range of prompting techniques for conversational pre-trained Large Language Models (LLMs).
2. The categorization approach is built on an interdisciplinary foundation and classifies prompting techniques into seven distinct categories:
a. Logical and Sequential Processing
b. Contextual Understanding and Memory
c. Specificity and Targeting
d. Meta-Cognition and Self-Reflection
e. Directional and Feedback
f. Multimodal and Cross-Disciplinary
g. Creative and Generative
3. Each category represents high-level conceptualizations of the approaches and intended use. Real-world examples are provided for each prompting approach to illustrate their practical applications.
4. The framework aims to help practitioners navigate the complex landscape of prompt engineering and enable more effective utilization of LLMs in various applications.
5. Prompt engineering is crucial for guiding LLMs to generate specific, relevant outputs. The quality and structure of prompts directly influence the model’s performance.
6. Ethical considerations in prompt design are critical to ensure responsible and safe usage of LLMs, addressing issues like biased outputs, illicit prompts, and implementing trust layers.
7. The paper offers a non-technical perspective on prompting techniques, making it accessible to a broader, interdisciplinary audience.
8. The categorization framework empowers users to tap into the transformative potential of LLMs in their respective fields and encourages cross-disciplinary collaborations.
9. The survey serves as a valuable resource for practitioners and researchers, aiding in making informed choices when crafting prompts for a wide range of applications.
Logical and Sequential Processing Techniques:
enable LLMs to break down complex, multi-step reasoning tasks into manageable steps. These techniques are based on the idea of splitting the task into smaller components and have scientific reasoning and mathematical justification behind their application. They not only enhance the models’ problem-solving capabilities but also provide a framework for more intuitive and human-like reasoning.
Techniques in this category include:
1. Chain-of-Thought (CoT) Prompting: This technique prompts the model to think in a sequential manner by processing information step-by-step. Instead of jumping straight to a conclusion, the model is prompted to dissect complex queries into explainable intermediary steps, showing its work and logically reasoning through each step of problem-solving.
Example: To solve a math problem like integrating x² from 0 to 1, instead of simply asking for the result, you could prompt sequential thinking by adding: “Start by defining the function. Next, set the integration limits. Now, walk me through the integration process step by step.”
2. Chain-of-Thought Factored Decomposition Prompting: This technique combines the sequential reasoning of CoT prompting with factored decomposition, which involves breaking down complex tasks or ideas into smaller, more manageable parts. It guides the model to think sequentially while also decomposing and addressing each subcomponent of the given task.
Example: To explain the process of photosynthesis, you might structure the prompt to first ask the LLM to define all the main processes involved before detailing the steps sequentially, ensuring that each aspect is thoroughly decomposed and explained.
3. Tree-of-Thoughts (ToT) and Graph-of-Thoughts (GoT) Prompting: These techniques build upon CoT prompting to explore more diverse and creative pathways of thinking. ToT forms a decision tree to guide the model through various logical paths and enhance its decision-making processes, while GoT models prompts as graphs, facilitating intricate thought aggregation and manipulation.
Example: For brainstorming business strategies, you might start with a core idea (e.g., improving customer service) and then ask the AI to generate multiple branching strategies or “What if?” scenarios to explore various possibilities.
4. Skeleton-of-Thought (SoT) Prompting: This technique provides a segmented yet comprehensive framework for responses, paralleling human problem-solving. By giving the model a structured, high-level “skeleton” of the desired response, it can ensure proper filling of the provided template without generating content too far outside the bounds of the desired output.
Example: To draft a business email, you could provide a template like “[Greeting], [Introduce topic], [Main content], [Closing remarks], [Signature],” prompting the model to fill in each section without omitting important parts or adding more than initially desired.
These Logical and Sequential Processing techniques enhance LLMs’ ability to handle complex reasoning tasks, making their problem-solving more transparent, structured, and human-like.
Contextual Understanding and Memory Techniques:
enable LLMs to maintain relevance and coherence throughout extended dialogues by recalling and referencing previous interactions. These techniques allow models to exhibit advanced levels of contextual understanding and memory, providing a seamless conversational experience.
Techniques in this category include:
1. In-Context Prompting: This technique emulates a form of “memory” for the model by maintaining context over multiple interactions. It integrates historical context into current responses, leveraging the significance of attention mechanisms to enhance the coherence of sequences in LLMs. By providing information within the current context window, prompt engineers can build upon previously introduced information to construct more complex ideas and scenarios.
Example: When conversing with a virtual assistant about planning a trip, if you initially mention traveling to Paris and later ask about “recommended restaurants,” the model will remember your past interactions and likely suggest eateries in Paris.
2. Multi-Personas Prompting: This technique utilizes an LLM’s ability to exhibit consistent character or persona traits, making the model “wear different hats” during an interaction. It enhances user experience by offering a stable conversational partner who is simultaneously adaptable to various voices, perspectives, and expertise.
Example: To see how differently Shakespeare and a scientist might describe a sunset, you could first ask, “How would Shakespeare describe a sunset?” and then follow up with, “Now how would a scientist describe a sunset?” The model can switch between the poetic persona of Shakespeare and the analytical persona of a scientist, providing different perceptions of the same topic.
3. Conversational Prompting: This technique involves crafting prompts that mimic natural human conversation, improving the fluidity and relevance of the model’s responses. Encouraging back-and-forth responses leads to richer, more nuanced interchanges, marking a shift from viewing AI interactions as singular queries to ongoing dialogues.
Example: When packing for a trip, instead of asking a singular question like “What is the weather like in Athens?”, you might follow up with “What should I pack for a day of sightseeing?” or “Any cultural norms I should be aware of?” to foster a more thorough and freeform experience.
4. Socratic Prompting: This technique emulates the Socratic method of dialogue by asking a series of questions to lead the model (or the user) to a conclusion or realization. It allows the user to explore the depth of knowledge an LLM has around a certain topic by probing into particular areas of interest.
Example: To explore the concept of justice, you may start with a general question like “What is justice?” and then follow up with more nuanced questions based on the model’s answers, such as “Is it always aligned with legality?”
These Contextual Understanding and Memory techniques enhance LLMs’ ability to maintain coherence and relevance throughout extended dialogues, providing a more natural and engaging conversational experience for users.
Specificity and Targeting techniques:
enhance the ability of LLMs to produce precise, goal-oriented responses. These techniques enable models to distinguish between different types of information delivery, focus on specific response targets, and align with overarching objectives, improving their utility and applicability in various domains requiring detailed and targeted information processing.
Techniques in this category include:
1. Show-me versus Tell-me Prompting: This technique involves instructing LLMs to either demonstrate (“show”) or describe (“tell”) indicated concepts. The success of this approach depends on the user’s ability to discern what type of information would be most valuable or which output would best suit the task at hand. Using both methods simultaneously or back-to-back can demonstrate the model’s ability to efficiently understand and adapt to nuanced differences in requests for forms of information delivery.
Example: To learn about photosynthesis, you might ask the model to either “Show me how photosynthesis works with a diagram” (yielding a graphical representation) or “Tell me about the process of photosynthesis” (providing a textual explanation). Your ideal output format and predictions of the model’s output should inform the initial request.
2. Target-your-response (TAR) Prompting: This technique directs the model to focus its responses toward specific targets or objectives, enhancing the relevance and brevity of its output. It emphasizes the importance of clearly indicating the format or style of the desired response.
Example: Instead of simply asking “Tell me about coffee,” you might say, “Provide a 3-sentence summary about the origins of coffee.” The latter prompt more carefully guides the model to a specific query and provides enhanced contextual bounds for its response.
3. Prompt Macros and End-goal Planning: These techniques utilize pre-defined prompt templates to establish overarching goals for the model’s responses. They ensure the LLM serves the broader goal with each interaction by combining potentially numerous “micro” prompts and queries into a single larger, “macro” prompt. The macro prompt must be broad enough to encompass the breadth of all desired micro queries while also being specific enough for the model to understand all relevant details of the implied requests.
Example: To have the model help you plan a trip within a single interaction, you might use a macro prompt like “Plan a 5-day trip to Colorado in January.” The model is likely to interpret this request with the appropriate level of nuance to provide itinerary, lodging, and activity suggestions in sequence.
4. Contrastive Prompting: This technique involves asking the model to compare or contrast two concepts, objects, or ideas. By framing a prompt in this manner, the model is tasked with identifying differences or similarities between the provided subjects, allowing for a deeper understanding of their characteristics and relationships.
Example: To better understand the differences between Python and Java programming languages, you might prompt the model to “Compare and contrast Python and Java programming languages.” This approach can provide insights into their syntax, performance, and use cases, helping you make informed decisions about which language to choose for your specific project.
Specificity and Targeting techniques enhance LLMs’ ability to provide precise, goal-oriented responses, making them more adaptable and effective in various domains requiring detailed and targeted information processing.
Meta-Cognition and Self-Reflection Techniques:
delve into the self-analytical capabilities of LLMs. By empowering these models to engage in self-guided learning, anticipate user needs, and generate programming code, these methods broaden the scope of LLM applications and enhance their interactive and assistive capabilities, making them indispensable tools in various technology-driven fields.
Techniques in this category include:
1. Self-reflection Prompting: This technique allows the model to evaluate its own responses and critically process its answers. It is particularly helpful when dealing with intricate tasks that were not initially prompted in a manner conducive to the model’s ability to work through each step methodically. By prompting the LLM to self-reflect on previous outputs, the model can review and update content to provide more deliberate and thoughtful responses.
Example: If a model has given a questionable answer on an ethical matter, you might follow up with, “Are you sure about that?” This would prompt the model to self-reflect on its previous response, potentially leading to a closer inspection of the initial question and either a newly updated answer or detailed reasoning for the first.
2. Meta-Prompting: This technique involves guiding LLMs to reflect on their own processes and methodologies for responding to prompts. It enhances the model’s understanding of its capabilities within the current context window and improves interaction quality by encouraging self-awareness and introspection into the process of interacting with LLMs.
Example: To learn how to write better prompts, you might ask the model to “Analyze the effectiveness of the last 5 prompts given to you and suggest improvements.” By introducing the prompting process into the current context, the model can provide insights into improving prompt design to maximize beneficial outputs from LLMs.
3. Anticipatory Prompting: This technique enables AI models to foresee and address future queries or needs based on the current context. It involves crafting prompts that encourage the model to provide both direct answers and related insights, anticipating possible follow-up questions or concerns.
Example: When asked, “How do I plant tomatoes?” the model might extrapolate from the context that you are new to growing tomatoes and also provide tips on dealing with common pests in anticipation of a potential follow-up concern.
4. Prompt to Code: This technique focuses on instructing the AI to generate functional programming code based on specific prompts. It capitalizes on the programming information contained within the model’s training data to understand and produce code snippets according to the language, formatting, and other requirements delineated by the user.
Example: To have a model generate code for a development project, you might ask the LLM to “Generate a Python function to calculate the Fibonacci series for a given number.” With this direction, the model would produce the relevant code according to your specifications.
Meta-cognition and Self-Reflection techniques enhance LLMs’ self-analytical capabilities, enabling them to engage in self-guided learning, anticipate user needs, and generate programming code. These techniques make LLMs more versatile and effective in various technology-driven fields, enhancing their interactive and assistive capabilities.
Directional and Feedback Techniques:
guide an LLM towards specific tasks or refine its responses based on user feedback. These techniques foster a balance between guided output and AI autonomy, encouraging creative and tailored responses while ensuring outputs align with user expectations and needs. By employing responsive feedback, users can actively participate in the model’s learning process, significantly enhancing the LLM’s adaptability and precision in various applications.
Techniques in this category include:
1. Responsive Feedback Prompting: This technique incorporates feedback directly into the prompting process to improve the quality and relevance of LLM responses. Feedback is given immediately after the initial model output to guide subsequent responses.
Example: When using a model to brainstorm ideas for a logo, if the model produces a mostly satisfactory result but you would like a few changes, you could prompt the model with feedback like “I like the color scheme, but can the design be more minimalistic?” This gives relevant input that allows the model to modify its next output to be more in line with your preferences.
2. Directional Stimulus Prompting: This technique involves using hints or subtle cues to steer the LLM in a desired direction without explicitly dictating the output. It is particularly useful when desiring unexpectedness and enhanced creativity in the model’s response. By only hinting at what you desire in the output, the model is left to fill in the blanks and make a best guess.
Example: When prompting a model to generate a story, instead of describing the plot line in detail and receiving a story entirely in line with your requests, you might include a hint like “Add an element of surprise when the hero meets the princess.” This allows you to indicate that you would like something unexpected to happen without specifying exactly what, nudging the LLM to incorporate an unspecified twist while leaving you in suspense of the specifics.
3. Ambiguous Prompting: This technique involves the intentional use of vague prompts to stimulate creativity or broad spectrums of responses from the model. Similar to Directional Stimulus Prompting, it is intentionally designed to be even more open-ended to encourage a substantial level of creativity and unguided generation. Ambiguous prompts are best used when you are either unsure of what you’re looking for in a response and/or would like to see unbiased and relatively unprompted ideas.
Example: To prompt a model to generate a story, instead of requesting a specific narrative through a detailed prompt like “Write a story about a knight saving a princess from a dragon,” you might simply request “Write a story about bravery.” By keeping the prompt vague, you reduce the amount of context given to the LLM and instead encourage the output of a narrative uninfluenced by specific user preferences.
Directional and Feedback techniques guide LLMs towards specific tasks or refine their responses based on user feedback. By employing these techniques, users can actively participate in the model’s learning process, fostering a balance between guided output and AI autonomy while enhancing the LLM’s adaptability and precision in various applications. These techniques encourage creative and tailored responses that align with user expectations and needs.
The Multimodal and Cross-Disciplinary:
category encompasses techniques that integrate various modes of input and diverse knowledge domains to enhance the versatility and applicability of LLMs. By combining multiple input types and blending knowledge from various disciplines, these techniques cultivate the depth and breadth of LLM responses and open up new possibilities for applications in diverse and complex scenarios, ranging from artistic endeavors to scientific research and historical analysis.
Techniques in this category include:
1. Multimodal Prompting: This technique refers to the use of diverse input types in prompting LLMs. Some models can now process more than just text, receiving prompts containing various combinations of words, images, audio files, and even videos to help provide context and guidance for the LLM’s output.
Example: To have a model write a poem about a particular photo of a sunset, it is important that the model incorporate details specific to the image. With a model capable of receiving multimodal inputs, you might upload your photo of a sunset and simultaneously ask the model to “Describe this scene by writing a poem.” By processing the photo with computer vision capabilities and the user request through the LLM, the model will have an understanding of both the visual and textual inputs to complete the request.
2. Cross-disciplinary Prompting: This technique involves blending knowledge from multiple separate disciplines to prompt unique solutions to interdisciplinary problems. By equipping LLMs with richly tailored tools and knowledge, they become more adept at handling complex queries and tasks that necessitate the inclusion of insights from various domains. This not only enhances their accuracy and relevance in these targeted areas but also opens up new possibilities for their application in various professional and academic fields more generally.
Example: As an avid fiction reader taking a class in physics, you might struggle to understand some of the more complicated scientific topics, as they’re conceptually very different from the literature you’re most interested in. You could prompt a model to “Explain quantum physics principles using analogies from classical literature.” By asking the model to explain one discipline through the lens of another, you expect the model to understand both domains well enough to provide a nuanced yet accurate interpretation of disparate concepts to connect the dots in your understanding.
3. Historical Context, Visual, and Modular Prompting: These techniques focus on embedding historical context, visual elements, and modular constructs into prompts. A historical context prompt might ask the model to answer in accordance with a particular historical setting or reference, while visual prompting might involve using images to guide the model’s responses, and modular prompting leverages structured, component-based prompts to address complex queries.
Example: If you’re curious about how your great-grandfather might view modern technology, you could prompt the LLM to “Describe the Internet as someone from the 1920s would understand it.” By specifying the historical setting (in this case, a particular decade) for the output, the model is able to better contextualize its response.
Multimodal and Cross-Disciplinary techniques integrate diverse input modes and knowledge domains to boost the versatility and expand LLM applications across contextually complex scenarios. By combining multiple input types and blending knowledge from various disciplines, these techniques enhance the depth and breadth of LLM responses, opening up new possibilities for their application in diverse and complex scenarios.
Creative and Generative techniques :
enable LLMs to generate creative content and elicit diverse, innovative, and thought-provoking responses. By employing a range of prompting strategies, from encouraging ambiguity to imposing lexical constraints, these techniques expand the capabilities of LLMs in creative tasks and open up new avenues for their application in diverse domains like storytelling, educational content generation, and creative writing.
Techniques in this category include:
1. Flipped Interaction Prompting: This technique reverses the conventional model-user dynamic. Instead of the user leading and directing the conversation with the model, the model might pose questions or seek clarifications from the user. By switching up the expected roles of the interaction, the model can guide the user to create their own outputs through targeted queries. This back-and-forth is helpful for structured, conversational brainstorming sessions and allows the user to leverage the LLM to prompt their own development of responses while maintaining full creative control over the outputs of the interactions.
Example: When drafting a business plan, rather than providing details to the model and having it generate suggestions, you might prompt the model to ask you questions to guide your own creation of the business plan by telling the model to “Ask me questions one-by-one to guide me in creating a comprehensive business plan.” In response, the model might begin asking questions like “What’s your business’s main objective?” or “Who is your target audience?” to prompt you to consider various aspects of business plan development.
2. Grammar Correction: Grammar correction prompts are specific instructions given to a model to identify and rectify grammatical errors or inconsistencies in the text provided by a user. LLMs can leverage their well-versed language understanding capabilities to serve as a conversational grammar checker, offering suggestions and improvements to the user’s input according to the requested output requirements. By asking the LLM to make improvements to verbiage and writing style, users can enhance the quality and clarity of their written communication and easily adapt their writing to various use cases and contexts.
Example: When writing a formal report, you want to ensure that your grammar and tone are appropriate for the given context. You might ask the model to “Review the following report for proper grammar and ensure that the language is clear, but professional.” By requesting both tonal and grammatical improvements, the model is given appropriate contextual bounds by which it may analyze and adjust the provided text.
3. Constrained Vocabulary Prompting: This technique involves restricting the model’s response to a specific set of words or a defined vocabulary. It places tangible limitations on the desired output of the model, ensuring that the generated content adheres to a predefined lexicon. By constraining the vocabulary, you gain greater control over the language generation process, making it particularly useful in scenarios where precision, adherence to specific terminology, or the avoidance of sensitive or inappropriate language is of utmost importance.
Example: When developing a chatbot for a customer service application in the healthcare industry, to ensure the bot provides accurate and consistent responses, you might instruct the model to “Answer customer queries using only medical terminology and avoid colloquial language.” This approach encourages the model to maintain a professional and medically accurate tone, reducing the risk of misunderstandings or misinformation when interacting with users seeking healthcare-related information or assistance.
Creative and Generative techniques enable LLMs to generate creative content and elicit diverse, innovative, and thought-provoking responses. By employing a range of prompting strategies, these techniques expand the capabilities of LLMs in creative tasks and open up new avenues for their application in diverse domains, enhancing the model’s ability to engage in structured brainstorming, provide grammar corrections, and adhere to specific vocabularies as needed.
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
The key points from the paper “A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications” are:
1. Prompt engineering has emerged as a critical technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs) without modifying model parameters. It leverages task-specific instructions called prompts.
2. The paper provides a structured overview of 29 recent prompt engineering techniques, categorized by application area. For each technique, it summarizes the methodology, applications, models involved, datasets used, and strengths and limitations.
3. Key application areas covered include:
- New tasks without extensive training (zero-shot, few-shot prompting)
- Reasoning and logic (chain-of-thought, logical prompts, etc.)
- Reducing hallucination (retrieval augmented generation, verification)
- User interaction (active prompting)
- Fine-tuning and optimization
- Knowledge-based reasoning
- Improving consistency and coherence
- Managing emotions and tone
- Code generation and execution
- Optimization and efficiency
- Understanding user intent
- Metacognition and self-reflection
4. The paper includes a taxonomy diagram organizing the techniques and a summary table detailing datasets, models, and key points for each technique.
5. While prompt engineering has enabled significant successes, challenges remain around biases, factual inaccuracies, and interpretability gaps. Future trends like meta-learning and hybrid prompting show potential. Ethical considerations in development and deployment are crucial.
Here are the 29 prompt engineering techniques categorized by application area as mentioned in the paper:
1. New Tasks Without Extensive Training
— Zero-shot Prompting
— Few-shot Prompting
2. Reasoning and Logic
— Chain-of-Thought (CoT) Prompting
— Automatic Chain-of-Thought (Auto-CoT)
— Self-Consistency
— Logical Chain-of-Thought (LogiCoT) Prompting
— Chain-of-Symbol (CoS) Prompting
— Tree-of-Thoughts (ToT) Prompting
— Graph-of-Thought (GoT) Prompting
— System 2 Attention Prompting
— Thread of Thought (ThoT) Prompting
— Chain of Table Prompting
3. Reduce Hallucination
— Retrieval Augmented Generation (RAG)
— ReAct Prompting
— Chain-of-Verification (CoVe)
— Chain-of-Note (CoN) Prompting
— Chain-of-Knowledge (CoK) Prompting
4. User Interaction
— Active-Prompt
5. Fine-Tuning and Optimization
— Automatic Prompt Engineer (APE)
6. Knowledge-Based Reasoning and Generation
— Automatic Reasoning and Tool-use (ART)
7. Improving Consistency and Coherence
— Contrastive Chain-of-Thought (CCoT) Prompting
8. Managing Emotions and Tone
— Emotion Prompting
9. Code Generation and Execution
— Scratchpad Prompting
— Program of Thoughts (PoT) Prompting
— Structured Chain-of-Thought (SCoT) Prompting
— Chain of Code (CoC) Prompting
10. Optimization and Efficiency
— Optimization by Prompting
11. Understanding User Intent
— Rephrase and Respond (RaR) Prompting
12. Metacognition and Self-Reflection
— Take a Step Back Prompting
Let’s understand each method.
Zero-shot Prompting: Zero-shot prompting involves providing a task description in the prompt without any labeled examples. The model relies on its pre-existing knowledge to generate predictions based on the given prompt. This technique allows LLMs to handle novel tasks without the need for task-specific training data.
Few-shot Prompting: Few-shot prompting involves providing the model with a small number of labeled examples (typically 1–5) in the prompt to demonstrate the desired task. By including a few high-quality examples, LLMs can better understand the task and improve their performance compared to zero-shot prompting.
Chain-of-Thought (CoT) Prompting:
CoT prompting encourages LLMs to generate step-by-step reasoning before providing the final answer. It guides the model through a logical reasoning chain, resulting in more structured and thoughtful responses.
Automatic Chain-of-Thought (Auto-CoT):
Auto-CoT automates the process of generating reasoning chains by instructing LLMs with a “Let’s think step-by-step” prompt. It samples diverse questions and generates multiple reasoning chains for each, forming a final set of demonstrations to enhance few-shot learning.
Self-Consistency:
Self-consistency is a decoding strategy that generates diverse reasoning chains by sampling from the language model’s decoder. It identifies the most consistent final answer by marginalizing the sampled chains, enhancing reasoning performance compared to greedy decoding in CoT prompting.
Logical Chain-of-Thought (LogiCoT) Prompting:
LogiCoT applies principles from symbolic logic to verify each step of reasoning generated by the model. It provides targeted feedback to revise incorrect steps through a think-verify-revise loop, reducing logical errors and hallucinations.
Key Aspects of LogiCoT Prompting:
- Think: Break down the problem into a series of logical steps or premises that can lead to a conclusion.
- Verify: Check each step for logical consistency and accuracy, using principles of symbolic logic.
- Revise: If errors or inconsistencies are found, revise the reasoning before proceeding to the next step.
Through this process, LogiCoT Prompting not only ensures that each step of the reasoning is logically sound and verifiable but also provides a clear, structured path to the final answer that can be easily followed and understood.
Tree-of-Thoughts (ToT) Prompting:
ToT manages a tree structure of intermediate reasoning steps, allowing LLMs to deliberately reason by assessing the progress generated by thoughts in solving the problem. It integrates the model’s abilities to produce and evaluate thoughts with search algorithms for systematic exploration among reasoning chains.
Graph-of-Thought (GoT) Prompting:
GoT models the reasoning process as a directed graph, allowing dynamic interplay, backtracking, and evaluation of ideas. It aggregates and combines thoughts from various branches, capturing the non-linear characteristics of human thinking.
System 2 Attention Prompting:
System 2 Attention utilizes the reasoning abilities of LLMs to selectively attend to relevant portions by regenerating the input context. It employs context regeneration and response generation with refined context to enhance attention and response quality.
Thread of Thought (ThoT) Prompting:
ThoT examines extensive contexts in manageable segments for incremental analysis. It summarizes and examines each segment before refining the information for a final response, enhancing reasoning in chaotic contexts.
Through the Thread of Thought Prompting process, the model systematically deconstructs and analyzes complex input (in this case, varied customer reviews) segment by segment. By summarizing each part, refining the gathered information, and then integrating these insights, ThoT Prompting enables the model to produce a nuanced and comprehensive understanding of the overall sentiment and themes within the customer feedback.
Chain of Table Prompting:
Chain of Table uses step-by-step tabular reasoning by dynamically generating and executing common SQL/DataFrame operations on tables. The iterative process enhances intermediate results, empowering LLMs to make predictions through logically visualized reasoning chains.
Through these steps, Chain of Table Prompting allows the model to break down a complex query into simpler, sequential operations that it can perform on the tabular data. This process not only makes it easier for the model to derive accurate answers but also provides a clear, logical reasoning path that can be audited and understood by humans.
Reduce Hallucinations:
Retrieval Augmented Generation (RAG):
RAG integrates information retrieval into the prompting process to reduce hallucinations and generate accurate responses. It analyzes user input, creates a targeted query, searches a pre-built knowledge base for relevant resources, and incorporates the retrieved snippets into the original prompt.
ReAct Prompting:
ReAct enables LLMs to generate reasoning traces and task-specific actions concurrently. It interleaves reasoning and action, allowing the model to induce, track, and update action plans while handling exceptions.
Scenario: Organizing a Community Cleanup Event:
Task
Plan and execute a community cleanup event in a local park, including recruiting volunteers, gathering supplies, and managing the event day logistics.
Through the ReAct Prompting approach, each step involves both thinking about the next logical step and taking concrete actions toward the event’s success. When an unforeseen issue arises, such as the weather forecast, the model demonstrates its ability to adapt the plan dynamically.
Chain-of-Verification (CoVe):
CoVe involves a systematic four-step process to reduce hallucinations:
1. The model generates baseline responses.
2. It plans verification questions to check its work.
3. It answers the questions independently.
4. It produces a revised response incorporating the verification.
Chain-of-Note (CoN) Prompting:
CoN improves the robustness of retrieval-augmented language models by handling noisy, irrelevant documents and accurately addressing unknown scenarios. It systematically evaluates document relevance, emphasizing critical and reliable information to filter out irrelevant content.
Chain-of-Knowledge (CoK) Prompting:
CoK breaks down complex tasks into well-coordinated steps. It starts with a comprehensive reasoning preparation stage, establishing context and framing the problem. Then, it engages in a dynamic knowledge adaptation phase, gathering evidence from various sources such as an internal knowledge base, external databases, and the given prompt.
These techniques aim to reduce hallucinations by integrating external knowledge, verifying generated responses, and dynamically adapting to relevant information sources.
Automatic Prompt Engineer (APE):
APE is an approach that automatically generates and selects the most effective prompts for specific tasks. Instead of relying on manually designed prompts, APE dynamically creates and optimizes prompts based on the task at hand. The process involves the following steps:
1. Analyze user input: APE examines the user’s query or task description to understand the nature of the task.
2. Generate candidate prompts: Based on the task analysis, APE creates a set of candidate prompts that could potentially guide the LLM to provide the desired output.
3. Prompt selection: APE employs reinforcement learning to evaluate the candidate prompts and select the most effective one for the given task.
4. Model execution: The selected prompt is used to guide the LLM in generating the final output.
Automatic Reasoning and Tool-use (ART):
ART enables LLMs to reason through multi-step processes and integrate external tools for enhanced problem-solving capabilities. It automates reasoning steps through structured programs and seamlessly incorporates external tool outputs into the reasoning process. ART involves the following key components:
1. Reasoning steps: ART breaks down complex problems into a series of reasoning steps, each represented as a structured program.
2. Tool integration: ART identifies the need for external tools or knowledge sources based on the reasoning steps and integrates them into the process.
3. Iterative execution: ART executes the reasoning steps iteratively, pausing the generation process to incorporate external tool outputs and resuming the flow as needed.
Example:
Consider a task that requires the LLM to answer a question about the distance between two cities.
User query: “What is the distance between New York City and London?”
By automating reasoning steps and integrating external tools, ART enables LLMs to handle complex problems that require specialized knowledge or computations beyond their native capabilities.
Emotion Prompting:
Emotion Prompting is a technique that aims to enhance the emotional intelligence and affective understanding of LLMs. It involves appending emotional stimulus sentences to the prompts to guide the model’s generation process. The emotional stimuli are designed to provide additional context and steer the model towards generating responses that are more empathetic, emotionally appropriate, and socially aware.
The process of Emotion Prompting involves the following steps:
1. Identify the desired emotional tone: Determine the emotional context or sentiment that should be reflected in the model’s response.
2. Select emotional stimulus sentences: Choose a set of sentences that convey the desired emotional tone. These sentences can be derived from psychological research or curated by experts.
3. Append the emotional stimuli to the prompt: Incorporate the selected emotional stimulus sentences into the prompt, typically at the beginning or end of the prompt.
4. Generate the response: Feed the emotionally enhanced prompt to the LLM and generate the response.
Emotion Prompting helps LLMs generate responses that are more emotionally aligned with the user’s needs and experiences. By incorporating emotional context into the prompts, the models can produce more empathetic, supportive, and socially appropriate responses.
Scratchpad Prompting:
Scratchpad Prompting is a technique that allows LLMs to generate intermediate results or “scratchpads” before providing the final answer. It enables the model to break down complex problems into smaller steps and perform arbitrary computations in the intermediate steps. The scratchpad serves as a workspace for the model to store and manipulate information during the problem-solving process.
Program of Thoughts (PoT) Prompting:
PoT Prompting combines the strengths of natural language reasoning and executable code. It allows LLMs to express reasoning through a combination of natural language explanations and executable Python code. The model generates a “program of thoughts” that includes both explanations and code snippets, which can be executed to arrive at the final answer.
Example:
Prompt: “Calculate the area of a circle with a radius of 5 units.”
Structured Chain-of-Thought (SCoT) Prompting:
SCoT Prompting is designed specifically for code generation tasks. It incorporates program structures (sequence, branch, and loop) into the reasoning steps, guiding LLMs to generate structured and logical code. SCoT Prompting explicitly considers the requirements from a source code perspective, improving the model’s code generation capabilities.
Chain of Code (CoC) Prompting:
CoC Prompting extends the idea of Chain-of-Thought (CoT) Prompting by leveraging code generation capabilities. It encourages LMs to format reasoning steps as executable code or pseudocode. CoC Prompting enables LMs to handle both logical and semantic tasks by simulating the reasoning process through code execution.
These prompting techniques showcase different approaches to generating code and executing it to arrive at the desired solution. They leverage the LLMs’ ability to understand and generate structured code, enabling them to tackle a wide range of programming and reasoning tasks.
Optimization by Prompting (OPRO):
OPRO is a technique that utilizes LLMs as optimizers by providing natural language prompts to iteratively generate solutions based on the problem description. Unlike traditional optimization methods, OPRO allows for quick adaptation to different tasks and customization of the optimization process through prompt engineering. The LLM generates candidate solutions, and the prompts guide the optimization process towards the desired outcome.
Rephrase and Respond (RaR) Prompting:
RaR is a prompting technique that allows LLMs to rephrase and expand questions to better understand user intent and provide more accurate responses. The LLM first rephrases the user’s question to resolve ambiguity and clarify the intent. Then, it generates a response based on the rephrased question. This two-step process helps in bridging the gap between the user’s thought process and the LLM’s understanding.
Take a Step Back Prompting:
Take a Step Back Prompting is a technique that enables LLMs to engage in metacognition and self-reflection by taking a step back from the problem at hand. It involves a two-step process: abstraction and reasoning. The LLM first extracts high-level concepts and principles from the specific problem instance (abstraction) and then applies those principles to solve the problem (reasoning). This approach helps LLMs to break down complex problems and apply general problem-solving strategies.
These prompting techniques showcase different approaches to optimizing LLM performance, understanding user intent, and promoting metacognition and self-reflection in problem-solving tasks.
Prompt Engineering Tools and Frameworks:
Langchain :
(https://langchain.readthedocs.io/, https://github.com/hwchase17/langchain)
Langchain is a comprehensive framework designed to assist in building applications with LLMs. It provides a set of tools and modules that help in managing and orchestrating the interaction between the application and the language model. Langchain offers features like Chains (for combining multiple components), Agents (for autonomous decision-making), and integrations with various data sources and APIs. It supports multiple programming languages and provides a flexible and extensible architecture for building LLM-powered applications.
Semantic Kernel:
(https://learn.microsoft.com/en-us/semantic-kernel/, https://github.com/microsoft/semantic-kernel)
Semantic Kernel, developed by Microsoft, is a framework that enables the integration of LLMs with conventional programming languages. It provides a set of APIs and tools to build intelligent applications using natural language processing and machine learning. Semantic Kernel supports skills development, planning, chaining, indexing, and memory access, making it easier to create complex and context-aware LLM applications. It also offers cross-platform support and can be used with multiple programming languages.
Guidance :
(https://microsoft.github.io/guidance/, https://github.com/microsoft/guidance)
Guidance is a library developed by Microsoft that provides a structured approach to prompt engineering. It introduces a templating language specifically designed for creating effective prompts. Guidance allows developers to define templates with variables, conditionals, and other control structures, making it easier to create dynamic and context-aware prompts. It integrates with various LLMs and supports multiple programming languages, providing a streamlined way to build prompt-driven applications.
Nemo Guardrails:
(https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/nlp/nemo_megatron/prompt_learning/guardrails.html, https://github.com/NVIDIA/NeMo-Guardrails)
Nemo Guardrails, developed by NVIDIA, is a library that focuses on building safe and controlled LLM applications. It provides a set of tools and techniques to ensure that the generated outputs align with predefined guidelines and constraints. Nemo Guardrails allows developers to define guardrails, which are essentially rules or filters that restrict the LLM’s output based on certain criteria. This helps in preventing the generation of inappropriate, offensive, or misleading content.
LlamaIndex:
(https://gpt-index.readthedocs.io/, https://github.com/jerryjliu/llama_index)
LlamaIndex is a library that provides a set of tools for efficient data indexing and retrieval in the context of LLM applications. It allows developers to create indices over large datasets, enabling fast and accurate information retrieval. LlamaIndex supports various indexing techniques, such as vector stores and keyword-based indices, making it easier to integrate external data sources with LLMs. It also provides utilities for data preprocessing, filtering, and aggregation.
FastRAG:
(https://github.com/microsoft/fastRAG)
FastRAG, developed by Microsoft, is a library that extends the basic RAG (Retrieval Augmented Generation) approach with advanced implementations. It provides optimized solutions for retrieval-augmented tasks, allowing LLMs to access and incorporate external knowledge effectively. FastRAG offers features like efficient indexing, similarity search, and context-aware retrieval, enabling the development of more informed and accurate LLM applications.
Auto-GPT
(https://docs.agpt.co/, https://github.com/Significant-Gravitas/Auto-GPT)
Auto-GPT is an open-source framework that simplifies the creation of autonomous AI agents using LLMs. It provides a high-level interface for designing and deploying agents that can interact with their environment, make decisions, and perform tasks independently. Auto-GPT supports various LLMs and offers a range of tools for agent development, including memory management, task planning, and execution monitoring.
AutoGen
(https://github.com/microsoft/autogen)
AutoGen, developed by Microsoft, is a framework that focuses on automating the process of prompt generation and optimization. It leverages LLMs to generate high-quality prompts based on user-provided examples and constraints. AutoGen uses techniques like few-shot learning and meta-learning to adaptively generate prompts that are tailored to specific tasks and domains. It aims to streamline the prompt engineering process and reduce the manual effort required to create effective prompts.
Useful Links:
https://platform.openai.com/docs/guides/prompt-engineering
Papers | Prompt Engineering Guide
A Comprehensive Overview of Prompt Engineering
www.promptingguide.ai
Conclusion:
Prompts are very important for getting good results from AI language models. As AI keeps getting better, making good prompts will be even more key. Many tools exist to help engineers create great prompts more easily. These tools speed up prompt engineering and provide guardrails. Prompt engineering is a rapidly growing field. Learning the techniques now will help you get the most out of AI models. Stay curious, and keep experimenting with prompts!
References:
I have provided the references above.
