Taking Control of AI with Prompt Engineering: Techniques for Tailoring Model Responses
Language models with exceptional comprehension could effortlessly manage tasks they had no prior experience performing. It can produce beautiful prose or solve complex issues without difficulty.
AI-enhanced entities capable of producing text for research without enough data are now possible, an incredible achievement in engineering involving AI entities capable of making texts without sufficient input. Even after extensive processing, they can generate enough results without adequate information input by humans. It truly shows what an incredible feat AI Prompt engineering is.
Language models have made great advances in natural language processing over recent years, thanks to cutting-edge AI capabilities like Zero-Shot Prompting and Few-Shot Prompting that take this discipline even further than before.
Some ground-breaking prompt engineering techniques have revolutionized how language models function. Methods that motivate language models to compose emotional poems about love without previously experiencing such themes and that assist in solving complex reasoning tasks through logical chains of logic are especially revolutionary.
Key Takeaways of the Article
- Prompt engineering allows language models to manage new tasks with extraordinary understanding, thanks to techniques like Zero-Shot Prompting or Few-Shot Prompting, which expand their capacity without prior exposure to functions.
- Chain-of-Though Prompting aids models in solving complex reasoning challenges step by step.
- Self-Consistency Prompting enhances reasoning performance by selecting answers with greater consistency.
- Generated Knowledge Prompting enhances models’ abilities to handle tasks that require commonsense knowledge.
- Tree of Thoughts Prompting expedites solutions to complex reasoning tasks. At the same time, Retrieval Augmented Generation produces text with more relevant and factual content by drawing upon external knowledge sources.
- Automatic Reasoning and Tool-Use Prompting assist models in complex tasks by guiding them through reasoning processes and tool usage. At the same time, Automatic Prompt Engineer Prompting automates prompt generation for other language models.
- Active-Prompting Prompting integrates APE and Chain-of-Thought prompts into complex task execution with external tool access. At the same time, Directional Stimulus Prompting guides models through reasoning processes with indirect cues and examples.
- Reactive Prompting integrates APE, Chain-of-Thought, and Automatic Reasoning to guide models and respond to feedback. Multimodal CoT Prompting helps models understand tasks with multimodal data more readily, whilst graph prompts assist models when producing graph-related output tasks.
- Prompt engineering holds immense potential to revolutionize language model capabilities in the future.
Let’s have a look at few of the Prompt Engineering Techniques as below:
1) Zero-shot prompting
Zero-shot prompting is an approach for teaching language models how to generate text for tasks they haven’t seen before by giving a prompt that describes but doesn’t demonstrate said task.
Zero-shot prompting involves training the language model first to produce text for predetermined tasks like poems, summaries and stories. Once complete, an unfamiliar task may be introduced that uses previous learning of known tasks to generate text for it effectively.
So, when new prompts arrive it can still produce results as its knowledge will help it produce text for them as it has done previously.
Example
Prompt – Write a poem about love.
AI Response –
Oh, love, how sweet thou art,
A feeling like no other.
It fills my heart with joy,
And makes my soul soar.
I love the way you make me feel,
So special and unique.
You are my everything,
And I can’t imagine my life without you.
Zero-shot prompting is a thrilling method that has the potential to produce text to suit a diverse variety of applications. Although it is still under development It is expected to revolutionize the way we interact with models of language.
2) Few-shot prompting
Few-shot prompting allows language models to generate text for tasks they only know a little about by providing prompts that describe examples relevant to that task alongside pertinent text.
Few-shot prompting is an efficient and versatile method that enables us to generate text for various tasks with limited or non-existent data available to us, making this technique particularly helpful when no large volumes can be readily analysed or tested.
Example
Prompt – Write a story about a character who is lost in the woods.
AI Response –
“The woods were dark and foreboding.”
“The trees seemed to reach out for me.”
“I was all alone.”
“…..”
3) Chain-of-thought-prompting
Chain-of-thought prompting is an aiding method designed to support language models with complex reasoning tasks. This prompting accomplishes its purpose by asking a question to address a particular challenge and offering examples arranged logically that illustrate what actions must be completed to overcome any hurdles.
Example
Prompt – Write a story about a character who is trying to solve a math problem.
AI Response –
“The problem was to find the sum of all the odd numbers between 1 and 100.”
“The character started by listing all the odd numbers between 1 and 100.”
“Then, the character added up all the numbers in the list.”
“The answer was 500.”
4) Self-consistency prompting
Self-consistency prompting is designed to increase language models’ performance on reasoning tasks. This method works by asking them to produce multiple responses for one prompt and selecting one that most closely conforms with all.
Example
Prompt – When I was 6, my sister was half my age. Now I’m 70 how old is my sister?
AI Response –
Answer 1: 35
Answer 2: 67
Answer 3: 70
The self-consistency prompt would then ask the language model to choose the answer that is the most consistent with all of the other answers. In this case, the answer 67 is the most consistent, because it is the only answer that is the difference between 70 and 3.
5) Generated knowledge prompting
Generated knowledge prompting is a technique in which language models create statements of knowledge generated from themselves and then use this additional input when responding to inquiries or answering queries. This approach helps these models perform more reliably on tasks requiring commonsense knowledge, such as question answering.
Example
Prompt – What is the capital of France?
Knowledge –
* The capital of France is Paris.
* Paris is a city in Europe.
* Paris is the most populous city in France.
* Paris is a major center of culture, commerce, and finance.
The language model would then be able to answer the question “What is the capital of France?” correctly, based on the prompt and the knowledge statements.
6) Tree of Thoughts Prompting
Language models use a Tree of Thoughts Prompting (ToT) to quickly complete complex reasoning tasks by breaking them down into manageable steps, like nodes in a tree representing individual thoughts or steps in the reasoning process. Once generated, text for every node is provided; this enables it to explore multiple potential solutions for solving an issue more rapidly than using standard approaches alone.
Example
Prompt – Write a story about a character who is trying to solve a math problem.
Tree of Thoughts –
- What is the math problem?
- What are the different steps involved in solving the problem?
- What are the different possible solutions to the problem?
- How can the character choose the best solution?
- How can the character implement the solution?
- What is the outcome of the problem?
Language models could then generate text for each node to explore various solutions to solving mathematical equations.
7) Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) is an approach that integrates large language models (LLM) with document retrieval systems to produce more informative and factual texts.
LLM generates text while the document retrieval system collects documents relevant to knowledge bases for reference during the text generation, helping ensure accurate and coherent results from generated texts.
Example
Prompt – Write a product description for a new type of electric car.
Retrieval Documents –
* Wikipedia article on electric cars
* Book reviews of electric cars
* Blog posts about the latest trends in electric cars
LLM would generate an incomplete and inaccurate product description due to insufficient access to retrieval documents. A document retrieval system would then be employed to access relevant knowledge base documents to create an accurate product description.
Documents generated as part of this process help inform its generation process, helping ensure LLM produces accurate and comprehensive product descriptions.
Based upon this final product description version developed, which would contain even more facts and figures than its initial draft form, more informative decisions would be made regarding product development decisions.
8) Automatic Reasoning and Tool-use
Automatic Reasoning and Tool-Use Prompting (ARTPP) is an automated reasoning and tool-use technique combined with prompt engineering. It assists language models in performing complex tasks by giving them prompts that guide them through their reasoning processes, helping them access external tools more readily or simplifying the job itself.
ARTPP helps language models work more efficiently by offering prompts to guide through each stage in this complex task-complexity analysis and help manage complex processes more efficiently.
Example
Prompt – Solve the Tower of Hanoi puzzle by breaking it down into a series of steps. Use a database of translations to help you solve the puzzle.
This prompt would tell the language model to use automatic reasoning to break the puzzle down into a series of steps. It would also tell the language model to access a database of translations to help it to solve the puzzle.
9) Automatic Prompt Engineer
Automatic Prompt Engineer (APE) Prompting is an emerging technique that employs a large language model to generate prompts for other language models automatically. Trained on a dataset of prompts and responses, APE learning allows it to recognize different elements that make up each prompt – including task, context and desired output -and output prompts more efficiently than any human could.
Once an APE model has been trained, it can generate prompts for other language models based on specific tasks they need to accomplish.
Example
Prompt – Write a story about a character who is lost in the woods and finds their way back to the trail. The story should be set in a forest in the Pacific Northwest and should include a description of the different plants and animals that the character encounters.
This prompt would prompt APE model users to create a tale centered around a character who becomes disoriented in the woods but finds their way back towards a trail, complete with details about various plants and animals they come across along the journey. Additionally, Pacific Northwest Forest settings should be specified. Hence, the story includes details of all that the protagonist encounters along the journey.
APE model would then use this information to produce a tailored prompt for the language model. This prompt would enable a generation of stories about a character who gets lost in the woods but eventually finds their way back onto the trail.
10) Active-Prompt
Active-Prompt (AP) prompting is a hybrid between APE prompting and Chain-of-Thought prompting (CoT). AP prompting facilitates language models performing complex tasks by providing prompts that guide their reasoning process while helping them access external tools.
Example
Prompt – Write a story about a character who is lost in the woods and finds their way back to the trail. The story should be set in a forest in the Pacific Northwest and should include a description of the different plants and animals that the character encounters.
The chain of thought is as follows:
- Identify the different elements of a story, such as the setting, the characters, the plot, and the conflict.
- Generate a list of plants and animals that are found in the Pacific Northwest.
- Choose the plants and animals that would be most likely to be encountered by a character who is lost in the woods.
- Arrange the plants and animals in a way that creates a realistic and engaging story.
This prompt would encourage APE model writers to construct a narrative about an adventurer who becomes lost in the woods before finding their way back out again, set in Pacific Northwest forests with detailed accounts of plants and animals encountered along their journey back home. Furthermore, this prompt would guide language model writers as they write their stories to solve an existing problem in which APE model writers are immersed.
11) Directional Stimulus Prompting
Directional Stimulus Prompting (DSP) guides language models through their reasoning processes with prompts. DSP prompts usually have two parts – an indirect stimulus cueing what to do next. At the same time, its example gives concrete instructions for doing it.
Example
Prompt – Generate a poem about love that is sad and romantic.
Directional Stimulus – Start by brainstorming a list of words that evoke the feeling of love.
Example – Some words that evoke the feeling of love include “heart,” “soul,” “passion,” and “romance.”
This prompt instructs a language model to begin brainstorming a list of words that evoke love feelings, with an example providing concrete instruction by listing some such examples.
12) ReAct Prompting
Reactive Prompting (ReAct Prompting) is an approach that integrates APE, Chain-of-Thought (CoT), and Automatic Reasoning (AR) prompting. ReAct Prompting can assist language models in performing complex tasks by giving them prompts that guide them through their reasoning process, assist with accessing external tools, or respond to feedback provided by them.
ReAct Prompting helps language models achieve these complex tasks more successfully by giving prompts that direct them through this challenging journey of discovery and provide responsive responses that reflect user responses.
Example
Prompt – Generate a poem about love that is sad and romantic. The poem should have a rhyme scheme of ABAB and a meter of iambic pentameter.
The chain of thought is as follows:
- Identify the different elements of a poem, such as the rhyme scheme, the meter, and the poetic devices.
- Generate a list of words that fit the rhyme scheme and meter.
- Choose the words that best convey the desired mood of the poem.
- Arrange the words in a way that creates a coherent and flowing poem.
The prompt also contains guidance about how the language model should respond to feedback; for instance, if its poem doesn’t seem sad enough, the prompt may inform it how to adjust for this and try again but make it more miserable than before.
React would use this data to generate a prompt for language modelling explicitly tailored to creating an emotional, romantic poem about love. A chain-of-thought model helps pinpoint all necessary steps to solve the problem. At the same time, feedback loops enable further refinements to its progress and identify any missteps along its journey.
13) Multimodal CoT
Multimodal CoT Prompting (M-CoT) is an approach that blends AP prompting with multimodal information for use by language models to complete complex tasks more successfully. M-CoT’s main use case is providing language models with prompts which guide their reasoning process while at the same time helping access external tools; multimodal information may further assist language models to gain a more profound understanding of tasks at hand and generate more engaging responses than before.
Example
Prompt – Write a story about a character who is lost in the woods and finds their way back to the trail. The story should be set in a forest in the Pacific Northwest and should include a description of the different plants and animals that the character encounters.
The multimodal information is as follows:
- An image of a forest in the Pacific Northwest.
- A video of a hiker walking through a forest in the Pacific Northwest.
- Audio recordings of the sounds of nature in a forest in the Pacific Northwest.
This prompt would direct the APE model to create a story revolving around a character who becomes lost in the woods before finding their way back onto a trail set within a forest in the Pacific Northwest, featuring various plants and animals encountered on their journey back home. Furthermore, multimodal information could help the language model better comprehend their task while producing more engaging stories for their readers.
APE model would then use this information to generate a prompt for the language model. The prompt would be explicitly tailored towards creating a story about an isolated character who finds their way back onto a trail, using multimodal information to identify all steps needed in solving the problem and in ordering those steps correctly.
14) Graph Prompts
Graph Prompts are effective prompts that utilize a graph to represent relationships among concepts, which may come in handy during tasks where graph-related output is desired, such as creating mind maps or decision trees.
Graph prompts are typically presented to language models in the form of nodes and edges – these represent different concepts and their relationships between themselves; then, they are asked to produce text describing this graph prompt.
Example
* Learning
* Types of learning
* Declarative learning
* Procedural learning
* Insight learning
* Factors that affect learning
* Motivation
* Attention
* Memory
This prompt would tell the language model to create a mind map about learning, with nodes representing concepts related to learning, including types, factors and influences affecting it and so forth. Edges would then show relationships among these concepts, like “types are affected by motivation” or “factors include attention”.
Language models would then be asked to generate text describing the graph; this text might take the form of traditional mind maps or another condition, including paragraphs or essays.
Final Thoughts
Prompt engineering encompasses techniques to maximize language models’ performance on various tasks. Zero-shot prompting allows models to generate text for unknown studies by drawing upon knowledge acquired during previous lessons.
Few-shot prompting is ideal for tasks requiring few examples; this allows models to generate text using just a handful of examples. Chain-of-thought prompting offers step-by-step guidance through logical examples for complex reasoning tasks.
Though still evolving, these techniques hold great promise to transform how we interact with language models, unleashing their full potential across various applications and problem-solving scenarios. Over time we can expect even more refined and efficient prompt engineering methods to emerge that further extend language models’ abilities.
