What is prompt engineering?

Prompt engineering is the optimization of prompts in language models (LMs) to build precise AI models and robust, innovative, future-forward applications.  

Craft prompts skillfully to push the limits of GenAI and uncover stunning possibilities to enhance your decision-making capabilities.  

What are some examples of prompt engineering offerings? 

To better understand inputs and generate unbiased, ethical, and contextually accurate prompts, any organization requires technically supercharged GenAI experts.

Some examples of prompt engineering offerings are: 

Prompt Optimization 
Automatically improve prompts for Large Language Models (LLMs), Language Models (LMs), ChatGPT, GPT 3.5, and Stable Diffusion models.  
Prompt Expansion 
Be prompt-perfect! Add that extra level of detail, and tailor it to fit your user’s needs through the skills and knowledge of GenAI experts.  
Prompt Adaptation 
Get more out of your AI models with less! Reduce the costs of your LLM API usage and shorten prompts through a Prompt Adaptation service. 
Prompt Personalization 
Craft prompts that power your models and workflows! Easily generate human-like content capable of engaging in conversations and solving problems.  
Prompt Refinement 
Score an ace with specificity! Let experts guide you into refining or fine-tuning your prompts to deliver clear and concise instructions for an amplified AI model performance.

What are some prompt engineering techniques?

Some prompt engineering techniques include -  

Zero-shot : This works in a way allowing language models to make predictions without requiring explicit additional examples. To put it simply, take the help of zero-shot prompting to generate a new answer not part of the training model e.g., like finding out if the contents in a paragraph have a positive or a negative connotation.
N-shot : It's a spectrum of approaches where N symbolizes the number of cues or examples provided to the language model for generating predictions. This helps with better in-context learning. E.g.: giving the meaning of a made-up word like ‘supercalifragilistic’ and asking it to write a sentence on its own.
Chain-of-Thought (CoT) : It urges an AI model to explain intermediate reasoning stages before providing the final answer to a multi-stage problem. This can also work to help with complex reasoning. E.g.: giving word problems for the model to solve.
Self-consistency : It involves crafting diverse paths of reasoning and selecting highly consistent answers. This, when with combined CoT, can help answer some interesting common sense and arithmetic questions.
Generated knowledge : It uses a large language model to produce valuable information relevant to a prompt. Generating knowledge before making a decisive prediction gives more depth to common sense reasoning questions.
Q&A : It allows crafting of thoughtful prompts so that language models can excel in tasks involving answering questions. Getting the model to provide specific answers can help improve the prompt thus get improved results.
Text summarization : It guides language models to generate precise and accurate summaries of longer texts. This can help summarize articles into short concise extracts that are quick reads.
Code completion : It involves sharing a partial code snippet to a language model that is then capable of generating suggestions or completing the code based on the provided input.
Code generation : It assists in generating code snippets or programming solutions by providing clear task specifications.

Why is AI-based personalization important today? 

AI-based personalization is an exciting development in today's digital landscape. It's crucial as it provides -  

Enhanced user experience 
Increased engagement and conversion 
Data-driven insights 
A competitive edge 
Efficient marketing and targeting 
Reduced information overload 
The building of brand loyalty 
Personalized learning and education

How to write better prompts?

Use these tips to write better prompts through prompt engineering.

Be as specific as possible : Give specific prompts to get more accurate and focused responses.
Describe your goal : Define exactly what kind of information you are looking for.
Mention your setting and provide the context : Describe your goal, target audience, and tone to get the most suitable answers.
Experiment with different prompt styles : Try different styles like asking to provide a summary, generate a list, or explain to a layman.
Iterate/refine to get delve deeper or get better results : Refine or modify your questions to attain better results.
Use your previous threads : Go back to specific discussions by using your previous threads.
Ask open-ended questions : Ask open-ended questions to get comprehensive responses.
Request examples : Ask for examples to better understand the concept or a particular topic.