Technology has redefined how the world works in this modern era, where Artificial intelligence (AI) has started to take centre stage in the recent years. 
In this evolving AI era,  Large Language Models (LLMs) like ChatGPT have revolutionized how we interact with technology. These models have the remarkable ability to understand and generate human-like text, making them invaluable tools in various domains. 
But how do we optimize their remarkable abilities to the maximum? The answer lies in prompt engineering – the strategic crafting of prompts to guide LLMs toward desired outputs.
In this blog post, we'll explore twelve simplified yet powerful prompt engineering techniques that enhance the functionality and usability of LLMs.

LLM or Large Language Model is a type of artificial intelligence model designed to understand and generate human-like text. These models are trained on large amounts of data and are capable of performing a wide range of natural language processing (NLP) tasks, including text generation, language translation, sentiment analysis, summarization, question answering, and more. 
One of the most prominent examples of LLMs is the GPT (Generative Pre-trained Transformer) series developed by OpenAI, known as ChatGPT.

Prompt Engineering is an art of crafting precise and effective prompts to enable LLMs like ChatGPT to generate desired responses. 
In simple words, it is the instruction or set of instructions given to LLMs so that they come up with the most relevant and accurate responses. 
Although generative AI is capable of understanding and generating human-like texts, it is better to prompt and direct them with instructions that are context-specific and precise so that they deliver the most accurate responses needed.

Here are 12 simplified and beginner-friendly prompt engineering techniques with examples:

Zero-shot prompting is a prompt engineering technique that allows LLMs to generate responses to tasks they haven't been explicitly trained on. It's like asking a friend a question on a topic they've never heard about, yet they manage to provide a reasonable answer based on their general knowledge.
With zero-shot prompting, we can provide a prompt to the LLM without adding any example or illustration, and it will use its general knowledge to produce a contextually appropriate response.
An example of zero-shot prompting on ChatGPT is:

Few-shot prompting takes zero-shot prompting a step further by providing the LLM with some examples or demonstrations of the desired task. It's like giving your friend a quick overview or specimen on a topic before asking them to provide insights.
With just a few examples, LLMs can quickly adapt to a new domain and generate output more specific and relevant to the subject matter.
An example of few-shot prompting on ChatGPT is:

Chain-of-thought prompting involves structuring prompts in a logical progression, guiding LLMs through a sequence of related concepts or ideas. It's like telling a story or explaining a concept step by step to help your friend understand better.
In simpler words, chain-of-thought prompting means breaking down a complex question into a series of smaller, simpler questions so that the LLMs generate responses maintaining continuity and relevance and manages to deliver the desired output.
An example of chain-of-thought prompting on ChatGPT is:

Tree-of-thoughts prompting expands on chain-of-thought prompting by organizing prompts in a hierarchical structure. It is used to generate coherent and structured responses by guiding them through a hierarchical structure of ideas or topics, resembling a tree-like branching structure.
This approach helps the model to stay focused on a specific topic and generate more contextually relevant responses.
An example of tree-of-thought prompting on ChatGPT is:
   • Root Topic: Climate Change
      o Branch 1: Causes           Leaf 1.1: Greenhouse Gas Emissions            Leaf 1.2: Deforestation
      o Branch 2: Effects            Leaf 2.1: Rising Temperatures            Leaf 2.2: Melting Ice Caps
      o Branch 3: Solutions            Leaf 3.1: Renewable Energy            Leaf 3.2: Carbon Capture Technology
Hence, the constructed prompts could be as follows:
   • "Discuss the role of greenhouse gas emissions in causing climate change."    • "Explain the impact of rising temperatures as a result of climate change."    • "Describe how renewable energy sources can mitigate the effects of climate change."

Output template prompting provides LLMs with predefined structures or formats for generating responses. It's like filling in the blanks of a template to create a specific type of document. With output template prompting, a structured template is specified for the LLMs in the input prompts.
This ensures a systematic and organized format for LLMs to produce desired outputs, making them suitable for various dynamic content generation.
An example of output template prompting on ChatGPT is:

Reframing prompt means modifying the original prompt and input the reframed prompt after the initial response. It is a technique used to modify or rephrase the input prompt in a way that encourages the language model to generate more diverse or nuanced responses.
This approach aims to guide the LLMs to generate responses based on the input prompts, taking into account the different contexts or perspectives provided by the original and reframed prompts.
An example of reframing prompt on ChatGPT is:
Original Prompt: What are the causes of climate change?

Reframed Prompt: How do human activities contribute to changes in Earth's climate?

Iteration means executing the same set of instructions a given number of times or until a specified result is obtained. It's like fine-tuning a musical instrument to produce the perfect melody.
This approach involves iteratively refining prompts based on the generated responses and incorporating insights gained from previous iterations to guide subsequent iterations. With each iteration, the prompts become more refined, leading to improvements in the quality of the generated responses.
An example of iteration prompting on ChatGPT is:
Original Prompt: What are some tips for maintaining a healthy diet?

Iterated Prompt: What are some nutritious foods that should be included in a balanced diet?

Role-play prompting immerses LLMs in simulated scenarios or personas. It's like asking your friend to pretend to be someone else for a role-playing game.
With role-play prompting, the LLMs are prompted to play a role or act as a particular character or persona and respond as if it was that particular character or persona involved in the scenario, adding a fun and creative twist to their interactions.
An example of role-play prompting on ChatGPT is:

Summarization prompting requires LLMs to condense and distil large amounts of information into concise summaries. With summarization prompting, a longer piece of text or a complex topic can be condensed into a shorter, more digestible form.
This technique is particularly useful for tasks such as document summarization, news aggregation, and information retrieval.
An example of summarization prompting on ChatGPT is:

Implicit prompting involves guiding LLMs to generate desired outputs without explicitly specifying the task or providing detailed instructions. Instead of directly stating what you want the model to do, implicit prompting relies on indirect cues, context, or partial information to steer the model towards the intended outcome.
An example of implicit prompting on ChatGPT is:

Perspective prompting involves crafting prompts or questions for LLMs in a way that encourages a particular viewpoint or perspective in the responses. It's like seeing things from someone else's perspective to gain a deeper understanding.
With perspective prompting, we can foster empathy and critical thinking skills in LLMs, enriching their understanding and expression of language.
An example of perspective prompting on ChatGPT is:

Self-ask prompting in prompt engineering is directed towards the users as a guide for them to provide additional information or clarify their request within the same interaction. In short, the user is asked to elaborate on their initial input or specify their needs further in order to generate the desired output.
Self-ask prompting enhances the effectiveness of natural language understanding systems in generating tailored and precise outputs. It's a way to encourage users to provide more context or details without explicitly asking them to do so in a separate step.
An example of self-ask prompting on ChatGPT is:

Mastering prompt engineering techniques is a pivotal skill for navigating the vast capabilities of Large Language Models (LLMs). Through this beginner's guide, we've explored foundational principles and practical strategies to craft prompts that optimize AI model responses effectively.
Prompt engineering techniques play a crucial role in enhancing AI interaction with LLMs like ChatGPT. By leveraging these techniques, we can enhance the functionality and usability of LLMs across various domains and applications and unlock a world of possibilities for communication, collaboration, and creativity.
With the knowledge gained from this guide and a commitment to continuous learning and refinement, beginners can embark on a journey of exploration and innovation, harnessing the potential of AI-driven interactions to solve problems, create content, and unlock new possibilities in an ever-evolving digital landscape. As we continue to explore and refine these techniques, the future of AI-driven innovation looks brighter than ever before.