Prompt Engineering is a process that allows generative AI models to assess and interpret texts to share or generate high-quality content and relevant images.
Crafting the right kind of prompts or using in-context learning or chain-of-thoughts prompting methods enables richer interaction with Large language models (LLMs).
What is Prompt Engineering? | Nitor Infotech
Delve into our insights on prompt engineering, its techniques, why AI-based prompting is required, and how to write better prompts. Nitor Infotech | US |India
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What are some Prompt Engineering Techniques?
Prompt Engineering techniques help guide generative artificial intelligence (GenAI) models to generate anticipated outcomes.
- 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 applied with 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 can aid in generating snippets of code or programming solutions by clearly specifying tasks.
Why do we need AI-based prompting?
AI-based personalization is an exciting development in today's digital landscape. AI based prompting acts as a bridge between humans and machines.
Crafting the right kind of prompts doesn’t only need sound technical knowledge but also some form of creativity. A few reasons why prompt engineering can clear the path for innovation include:
01
Accuracy
When training AI models it becomes important to create more relevant output.
An engineer needs to be precise and accurate while providing features and benefits to help the AI model describe the product better.
02
Efficiency
84% GenAI users believe that using it to automate client communication and other forms of mundane tasks has helped speed up their customer interactions.
AI based prompting can streamline repetitive tasks and provide the best possible response to general queries. This will help humans focus on creative tasks.
03
Personalization
Research shows that 68% GenAI users use it to create and personalize communication with clients.
AI based prompting can help work to suit individual users and their specific needs. This means improved CX and better personalization.
Learn to build products effectively and innovatively with Prompt Engineering.
Let’s Talk Prompt engineering!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 discussion 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.
Optimize prompts effectively with large language models to harness the potential of generative AI.
