DEV Community: SOMYA RAWAT

I Built a ‘Prompt Bible’ So You Don’t Waste Hours Talking to AI

SOMYA RAWAT — Tue, 20 Jan 2026 05:02:40 +0000

Let’s be real:
Most AI prompts online are either too vague (“Write better code”) or over-engineered theater.

So I compiled what actually works - the ChatGPT 5 Prompts Bible: a practical, field-tested library of prompts I use daily as a Technical Product Manager to:

✨ Generate clean user stories from rough ideas
🐞 Debug JavaScript/React errors with precise context
📄 Auto-draft API docs, PR descriptions, and release notes
💡 Ideate features based on real user pain points
🧪 Test edge cases in system design conversations

No fluff. No “act like a world-class CEO” nonsense. Just repeatable, copy-paste prompts that get accurate, actionable outputs.

This isn’t theory it’s the toolkit I built while shipping web apps, managing dev teams, and automating PM workflows.

👉 Grab it or book a 5-min walkthrough: https://topmate.io/somya_rawat_0007/1904263

P.S. Made for devs, PMs, indie hackers, and builders who want AI to save time, not create more noise.

#AI #PromptEngineering #Productivity #WebDev #TechTools #BuildInPublic #DeveloperTips #NoBS

The Impact of Artificial Intelligence on Everyday Life

SOMYA RAWAT — Thu, 11 Jul 2024 09:23:31 +0000

Artificial Intelligence (AI) has become a buzzword in today's tech landscape. Its applications are vast, and it is transforming various aspects of our daily lives. Here's a straightforward guide to understanding how AI is impacting our world.

What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science that aims to create machines capable of intelligent behavior. This involves developing algorithms and models that enable computers to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and decision-making.

Everyday Applications of AI

Personal Assistants
- Examples: Siri, Google Assistant, Alexa
- How They Help: These AI-powered assistants can perform tasks like setting reminders, answering questions, playing music, and controlling smart home devices.
Healthcare
- Diagnostics: AI can analyze medical images and data to assist doctors in diagnosing diseases more accurately and quickly.
- Personalized Treatment: AI algorithms can tailor treatment plans based on individual patient data, improving outcomes.
Transportation
- Autonomous Vehicles: Self-driving cars use AI to navigate roads, recognize traffic signals, and avoid obstacles.
- Traffic Management: AI helps in optimizing traffic flow and reducing congestion through smart traffic light systems.
Customer Service
- Chatbots: Many companies use AI-powered chatbots to handle customer inquiries, provide support, and improve response times.
- Personalized Recommendations: AI analyzes customer data to provide personalized product recommendations and enhance shopping experiences.
Finance
- Fraud Detection: AI can identify suspicious transactions and prevent fraud by analyzing patterns and behaviors.
- Automated Trading: AI algorithms can analyze market data and execute trades faster and more efficiently than human traders.
Entertainment
- Content Recommendations: Streaming services like Netflix and Spotify use AI to recommend movies, TV shows, and music based on user preferences.
- Gaming: AI is used to create intelligent and adaptive non-player characters (NPCs) in video games, enhancing the gaming experience.

Benefits of AI

Efficiency: AI can automate repetitive tasks, freeing up time for more complex activities.
Accuracy: AI systems can analyze large amounts of data with high accuracy, reducing human error.
Personalization: AI can provide personalized experiences and solutions based on individual preferences and behaviors.

Conclusion

Artificial Intelligence is no longer a concept of the future; it is already a significant part of our daily lives. From personal assistants to autonomous vehicles, AI is transforming various industries and improving the quality of our everyday experiences. As AI technology continues to advance, its impact will only grow, offering even more innovative solutions to the challenges we face.

By understanding the everyday applications of AI, we can better appreciate its potential and the ways it is shaping our world. Whether in healthcare, transportation, or entertainment, AI is making our lives more efficient, personalized, and secure.

Pioneering the Future: The Intersection of Technology and Entrepreneurship

SOMYA RAWAT — Tue, 16 Apr 2024 03:40:24 +0000

In the dynamic world of the 21st century, two domains have emerged as the driving forces of innovation and progress: Technology and Entrepreneurship. These fields, though distinct, often intersect in exciting and transformative ways.

The Dawn of Digital Entrepreneurship

The advent of the internet and digital technologies has given birth to a new breed of entrepreneurs. These digital pioneers leverage technology to create innovative solutions, disrupt traditional industries, and redefine the boundaries of what's possible.

From e-commerce platforms that revolutionize retail, to fintech solutions that democratize access to financial services, technology has become an indispensable tool in the entrepreneur's arsenal. It's not just about creating new products or services, but also about using technology to optimize operations, reach global markets, and deliver superior customer experiences.

The Role of Emerging Technologies

Emerging technologies like Artificial Intelligence (AI), Blockchain, and Internet of Things (IoT) are further pushing the envelope of innovation. AI is automating tasks, personalizing user experiences, and providing entrepreneurs with actionable insights. Blockchain is creating new opportunities for secure, transparent transactions and decentralized applications. IoT is connecting the physical and digital worlds, opening up a plethora of opportunities for smart solutions.

The Entrepreneurial Mindset in Tech

It's not just about the tools, though. The entrepreneurial mindset – characterized by risk-taking, resilience, and a relentless pursuit of opportunity – is equally important in the tech world. Whether it's a startup founder developing a groundbreaking app, or an intrapreneur driving innovation within a tech giant, this mindset is key to driving progress and staying ahead of the curve.

Conclusion

As we look to the future, the intersection of technology and entrepreneurship will continue to be a hotbed of innovation. It's an exciting space where creativity meets practicality, and where the entrepreneurs of today are shaping the world of tomorrow.

Business Intelligence and Analytics: A Comprehensive Guide

SOMYA RAWAT — Sat, 09 Dec 2023 02:12:06 +0000

Introduction

In the modern business landscape, data is king. With the advent of digital technology, businesses are generating and collecting more data than ever before. This data, when properly analyzed and utilized, can provide valuable insights that can drive business growth and efficiency. This is where Business Intelligence (BI) and Analytics come into play.

What is Business Intelligence?

Business Intelligence (BI) is a technology-driven process that involves the collection, integration, analysis, and presentation of business information. The purpose of BI is to support better business decision making. Essentially, Business Intelligence systems are data-driven Decision Support Systems (DSS).

BI encompasses various tools, applications, and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop, and run queries against the data, and create reports, dashboards, and data visualizations to make the analytical results available to corporate decision-makers as well as operational workers.

The Role of Analytics in Business Intelligence

While Business Intelligence provides the right information at the right time to the right people, Analytics goes a step further. Analytics is the systematic computational analysis of data or statistics. It involves studying past historical data to research potential trends, analyze the effects of certain decisions or events, or evaluate the performance of a given tool or scenario.

Analytics can be used to predict future trends or outcomes, which is known as predictive analytics, or understand the reasons behind a certain occurrence, which is known as prescriptive analytics.

The Power of BI and Analytics

Together, BI and Analytics provide powerful tools for businesses. They can:

Improve Decision Making: With accurate, up-to-date information, decision-makers can make informed choices that benefit the business.
Increase Operational Efficiency: BI and Analytics can identify areas where resources are being wasted or processes can be streamlined.
Identify New Opportunities: Through trend analysis and predictive analytics, businesses can identify new markets, customer segments, or product opportunities.
Gain Competitive Advantage: Understanding your own data as well as market trends can give businesses a competitive edge.

Conclusion

In the age of big data, Business Intelligence and Analytics are crucial tools for success. They allow businesses to navigate the complex landscape of modern business, making sense of data, and using it to drive growth and efficiency. Whether you're a small business owner or a leader in a large corporation, understanding and utilizing BI and Analytics can give you the tools you need to succeed in today's competitive marketplace.

Remember, data is only as good as what you do with it. So, harness the power of BI and Analytics, and let your data guide you to success. Happy analyzing!

The Environmental Impact of Tech: Navigating the Digital Carbon Footprint

SOMYA RAWAT — Sun, 06 Aug 2023 16:59:24 +0000

In today's digital age, where technology is an integral part of our lives, it's crucial to take a closer look at the environmental consequences of our tech-driven world. While technological advancements have undoubtedly improved our lives in countless ways, they also come with a significant environmental footprint that cannot be ignored. From manufacturing to usage and disposal, every stage of a device's lifecycle contributes to its overall impact on the planet.

1. Manufacturing and Resource Extraction:

The journey of a tech device begins with resource extraction. The mining of rare minerals and metals required for devices like smartphones, laptops, and batteries has far-reaching ecological consequences. Unsustainable mining practices can lead to deforestation, habitat destruction, and pollution of water bodies. As demand for tech devices continues to rise, responsible sourcing of materials becomes paramount.

2. Energy Consumption:

The energy consumption of tech devices and data centers is a major contributor to their environmental impact. Data centers, which power our online services and storage, require immense amounts of electricity to function. Additionally, the energy consumed during the manufacturing process of devices is substantial. The move towards renewable energy sources for both manufacturing and operations is crucial in mitigating this energy-related impact.

3. E-Waste and Disposal:

As devices become obsolete at an alarming rate, electronic waste (e-waste) has become a significant environmental concern. Discarded devices often contain hazardous materials that can leach into the soil and water, posing risks to both human health and the environment. Proper recycling and responsible disposal of e-waste are essential to prevent these negative impacts.

4. Extending Device Lifecycles:

One effective way to reduce the environmental impact of tech is to extend the lifespan of devices. Manufacturers can design products that are repairable, upgradable, and durable, enabling consumers to keep their devices functional for longer periods. This approach reduces the need for frequent replacements and helps curb the demand for new devices.

5. Circular Economy Approach:

The concept of a circular economy, where products are designed to be reused, refurbished, or recycled, offers a promising solution to the environmental challenges posed by tech. This approach encourages manufacturers to design products with the end of their lifecycle in mind, reducing waste and conserving valuable resources.

6. Cloud Computing and Energy Efficiency:

Cloud computing, while contributing to the tech industry's carbon footprint, also offers opportunities for energy efficiency. Cloud providers can optimize data center operations to reduce energy consumption and carbon emissions. Additionally, advancements in energy-efficient hardware and cooling technologies are helping to make data centers greener.

7. Consumer Awareness and Responsible Use:

Individuals can play a significant role in minimizing the environmental impact of tech. Being conscious of energy consumption, practicing responsible e-waste disposal, and supporting products with eco-friendly certifications can collectively make a difference. Choosing devices with longer lifecycles and considering second-hand options can also contribute to a more sustainable tech ecosystem.

In conclusion, the environmental impact of tech is a multifaceted challenge that requires collaborative efforts from manufacturers, consumers, and policymakers. As technology continues to evolve, it is crucial to prioritize sustainability at every stage of the tech lifecycle. By adopting greener practices, embracing renewable energy, and promoting responsible consumption, we can harness the benefits of technology without compromising the health of our planet. The future of tech lies in striking a balance between innovation and environmental stewardship.

The ethical implications of AI: Can we trust machines to make moral decisions ?

SOMYA RAWAT — Mon, 13 Mar 2023 04:29:40 +0000

Artificial Intelligence (AI) has come a long way since its inception. Today, AI has the ability to make decisions and predictions, automate tasks, and even interact with humans. However, with this great power comes great responsibility. The ethical implications of AI are becoming increasingly important as machines make decisions that can have significant impacts on human lives.

One of the most pressing ethical concerns surrounding AI is whether we can trust machines to make moral decisions. In this blog post, we will explore this issue in detail and examine the challenges and opportunities of trusting AI with moral decision-making.

Defining Moral Decision-Making :

Before delving into the question of whether we can trust AI with moral decision-making, it's important to define what we mean by "moral decision-making." Moral decision-making refers to the process of determining what is right and wrong, good and bad, just and unjust. It is the foundation of ethics, and it involves weighing various factors and considering their potential consequences.

Moral decision-making is not just a human phenomenon. Animals also exhibit some form of moral behavior, such as empathy and cooperation. However, the complexity of human morality goes beyond what animals are capable of, which is why the question of whether AI can make moral decisions is particularly pertinent.

The Challenges of Trusting AI with Moral Decision-Making :

There are several challenges associated with trusting AI with moral decision-making. Here are some of the most important ones:

1. Bias and Discrimination : AI is only as good as the data it is trained on. If the data contains bias or discrimination, the AI system will learn and reproduce those biases, leading to unfair or unjust decisions. This can have significant ethical implications, particularly in areas such as hiring, lending, and criminal justice.

2. Lack of Accountability : Unlike humans, machines do not have a moral compass or a sense of responsibility. This means that when an AI system makes a mistake or produces an unjust outcome, it is difficult to hold it accountable or assign blame. This can make it challenging to ensure that AI systems are fair and just.

3. Lack of Empathy : Empathy is a key component of moral decision-making. It allows us to put ourselves in someone else's shoes and consider their perspective. However, machines lack the ability to empathize, which means that they may not be able to take into account the emotional or social aspects of a decision.

4. Complex Decision-Making : Many moral decisions are complex and involve weighing multiple factors. For example, determining the best course of action in a medical emergency involves considering the patient's health, their values and beliefs, and the potential consequences of different treatment options. It is challenging to program AI systems to make such complex decisions.

The Opportunities of Trusting AI with Moral Decision-Making :

Despite the challenges, there are also several opportunities associated with trusting AI with moral decision-making. Here are some of the most important ones:

1. Consistency and Transparency : Machines are consistent and transparent in their decision-making. This means that they can apply the same standards and criteria across different cases, leading to more consistent outcomes. Additionally, the decision-making process can be made transparent, which can increase trust and accountability.

2. Efficiency and Accuracy : Machines can process vast amounts of data quickly and accurately. This means that they can analyze complex situations and make decisions based on a wide range of factors. This can lead to more efficient and effective decision-making.

3. Augmenting Human Decision-Making : AI systems can augment human decision-making by providing additional information and insights. This can help humans make more informed and ethical decisions.

Conclusion :

The ethical implications of AI are complex and multifaceted. While there are significant challenges associated with trusting AI with moral decision-making, there are also opportunities for AI to augment and improve human decision-making. To ensure that AI is used ethically, it is important to proceed with caution and to consider the implications of AI on a wide range of stakeholders. Ultimately, the goal should be to use AI in a way that benefits society as a whole, while ensuring that the technology is used in a fair and just manner.

Introduction to ReactJS and its key features

SOMYA RAWAT — Fri, 10 Mar 2023 12:02:44 +0000

ReactJS is an open-source JavaScript library used for building user interfaces. Developed by Meta, ReactJS is widely used by developers to create dynamic and interactive web applications. In this article, we will introduce you to the key features of ReactJS and why it has become such a popular choice for front-end development.

1. Virtual DOM :

One of the key features of ReactJS is its use of the Virtual DOM. The Virtual DOM is a lightweight representation of the actual DOM (Document Object Model) used by web browsers. In ReactJS, instead of making changes to the actual DOM, changes are made to the Virtual DOM first, and then the changes are propagated to the actual DOM. This approach significantly improves the performance of ReactJS applications as it minimizes the number of updates to the actual DOM, resulting in faster rendering and improved user experience.

2. Component-Based Architecture :

ReactJS is based on a component-based architecture. A component is a reusable piece of code that encapsulates the logic for a particular part of a user interface. ReactJS components can be nested, making it easy to create complex UIs. Components can be easily reused throughout an application, and their modular nature makes it easy to maintain and update the codebase.

3. JSX :

ReactJS uses JSX, a syntax extension to JavaScript that allows developers to write HTML-like code directly in their JavaScript code. JSX makes it easy to create and manipulate UI components. Developers can also create custom JSX elements that map to ReactJS components, making it easy to reuse code across different parts of an application.

4. Unidirectional Data Flow :

ReactJS follows a unidirectional data flow architecture. Data flows from the top-level components down to the child components. This approach ensures that the data is always consistent and predictable, making it easier to debug and maintain ReactJS applications.

5. State Management :

ReactJS components can have state, which is a JavaScript object that stores the component's data. When the state changes, ReactJS automatically re-renders the component, updating the UI to reflect the new state. ReactJS also supports lifting the state up to a common parent component, making it easier to manage the state of an application as it grows.

Conclusion :

ReactJS is a powerful and flexible library for building user interfaces. Its use of the Virtual DOM, component-based architecture, JSX, unidirectional data flow, and state management make it a popular choice for building dynamic and interactive web applications. If you're looking to learn front-end development or want to build modern web applications, ReactJS is definitely worth considering.

10 Golden Rules for Solving a Coding Question in an Interview :

SOMYA RAWAT — Thu, 22 Dec 2022 08:22:11 +0000

If we are dealing with top/maximum/minimum/closest 'K' elements among 'N' elements, we will be using a Heap.
If the given input is a sorted array or a list, we will either be using Binary Search or the Two Pointers strategy.
If we need to try all combinations (or permutations) of the input, we can either use Backtracking or Breadth First Search.
Most of the questions related to Trees or Graphs can be solved either through Breadth First Search or Depth First Search.
Every recursive solution can be converted to an iterative solution using a Stack.
For a problem involving arrays, if there exists a solution in O(n^2) time and O(1) space, there must exist two other solutions:
(1) Using a HashMap or a Set for O(n) time and O(n) space.
(2) Using sorting for O(n log n) time and O(1) space.
If a problem is asking for optimization (e.g., maximization or minimization), we will be using Dynamic Programming.
If we need to find some common substring among a set of strings, we will be using a HashMap or a Trie.
If we need to search/manipulate a bunch of strings, Trie will be the best data structure.
If the problem is related to a LinkedList and we can't use extra space, then use the Fast & Slow Pointer approach.

Roadmap To Becoming A Full Stack Web Developer :

SOMYA RAWAT — Thu, 22 Dec 2022 07:54:42 +0000

1. Choose a Technology

Full-stack development comprises front-end, back-end and database management.

Few technologies on which you can work and are trending:

MERN Stack
MEAN Stack

2. Frontend Development

Front-End is the User Interface, it deals with the website's overall appearance & how interactive it is.

Mastering the elements of HTML, CSS & JavaScript is essential.

2.1 Frontend Development

HTML: Start with creating a small webpage. Include; tables, images & links to make it more eye-catching.

CSS: Add colors, borders, build the navigation bar, play with the icons, font and text.

JavaScript: Learn about DOM and API.

3. Backend Development

Ace these skills:

Coding Languages: Python, Java and JavaScript
Frameworks: Express JS, Django and Ruby on Rails
Database Management Systems: MongoDB, MySQL
API: Application Programming Interface
Client-Server Architecture

4. Version Control

Using Git is very good practice if you are working on applications. It is an open-source platform where the entire code exists from the initial stage.

Some of them are:
GitHub, GitLab, Beanstalk, Mercurial

5. Build Projects

Post mastering the theory, the final stage is practicing and application of the knowledge acquired.

Practice, practice and practice more 👩‍💻

How Snapchat is using Machine Learning ?

SOMYA RAWAT — Tue, 14 Jun 2022 02:39:00 +0000

The very famous social media app Snapchat is all around the corner or let's in pocket of teens and even adults.

While the addiction is concerning or not is still debatable, something surely concerning is what's circulating around the app.

Creating Filters :

For filters, Snapchat uses AI-powered lenses with small machine learning models to detect a face, differentiate the structures and features within it, and then create a 3D model face.

This process is completed using "Image Processing", "Facial Landmarks" - plotting points on face and comparing it with collected data sets and active "Shape Modelling" which creates a virtual face and aligns it with the actual face.

Targeting Ads :

They call it as goal-based bidding which is an iterative improvement to advertising optimization on Snapchat.

The targeting technique allows advertisers to tell Snapchat whether they value ad engagement - such as swiping through to install an app, visit a website or watch a movie trailer - versus simply reaching the maximum number of users.

Avoiding Drug Deals :

Last October NBC News did a story about the troubling drug deals presumably taking place on the app. Which left everyone shocked.

In the report, it spoke about teens and young adults who had bought what they believed to be a prescription pill, but turned out to be a counterfeit pill containing deadly doses of fentanyl.

Since that report Snapchat has been ramping up its efforts to thwart drug deals on its platform.

Jan 18, Snap stated that it has a zero tolerance for drug dealing on Snapchat.

It states to deploy tools that proactively detect drug related content with the help of data, AI and ML in the matter, helping the law enforcement to support their investigations.

It will also provide in-app information and support to Snapchatters who search for drug-related terms through a new educational portal, Heads Up.

Snapchat's use of AI and ML proactively detects 88% of drug related content.

Snapchat Machine Learning :

Training Data -> Training Process -> .ONNX/.PB -> ML Components

Snap ML allows you to make your lenses even more intelligent with Machine Learning.

Just plug in an ML model to apply art style to the camera feed, use custom segmentation masks, attach images to custom detected objects, detected objects, understand what is in the scene and more.

Don't do these DATA SCIENCE Mistakes

SOMYA RAWAT — Fri, 13 May 2022 02:43:59 +0000

Beginner Mistakes :

Spending a lot of time on theory.
Jumping directly into coding ML algorithms without learning the prerequisites.
Thinking to build the future without knowing the basics.
Not spending enough time on exploring and visualizing the data.
Focusing on accuracy over understanding how the model works.
Assuming the algorithm is more important than domain knowledge.
Not having a structured approach to problem-solving.
Learning multiple tools at once.
Not learning/working consistently.
Less communication.

Intermediate Mistakes :

Data Leakage.
Sampling Bias.
Too many redundant features.
Bad Coding style.
Unavailability of features in future.
Not performing proper testing.
Not Choosing the Right Model- Validation Frequency.
Choosing the wrong tool to visualize.
Paying Attention Only to Data.
Building a Model on the Wrong Population.
Ignoring the probabilities.
Data Analysis without a Question/Plan.
Don't Sell Well.

Mistakes to avoid while applying for jobs :

Do Not Lie.
Using too many Data science Terms in your Resume.
Overestimating the value of academic degrees.
Do not narrow your search.
Competitions are not Real-Life.
Your LinkedIn profile is sacrosanct.
Being unprepared to discuss projects.

Mistakes to avoid during Interviews :

Not asking enough questions.
Discussing the old projects.
Not considering the business impact.
Not good at technical skills.
Not being a problem solver.
Not thinking from the interviewer perspective.
Not supporting your statements with stats and facts.
Failing to convey how you will help the company.
Forgetting The Requirement.
Not Using “I don't know" Judiciously.
Focusing on Answer Rather than Approach.
Not Taking the Opportunity to go into Details.
Taking Failure Personally.

10 wrong reasons to become a DATA SCIENTIST :

You Think Its Easier to get a Job.
No Interest in Coding or Programming.
Your Primary Reason is Money.
You Hate Math.
An Overall Lack Of Passion.
You Find Working With Data Annoying.
Consistent Learning Is Boring.
Lack of Communication Skills.
Hate Collaboratively Working in a Team.
Exploration And Working On Newer Projects does not really appeal to you.

How Machine Learning is used in E-Commerce ?

SOMYA RAWAT — Sat, 07 May 2022 04:59:23 +0000

Smart Search :

Many visitors to your online store will have an idea of what they need what they might not know is the name of specific product or even which item would meet their needs.

Give a large enough dataset, you could determine which results are better for people best match their needs on their previous behaviour.

By analyzing the data and figuring out what items go together, you can also recommend similar products, and even cross-sell items that are frequently bought together by your users.

Recommendations :

Personalization and recommendation engine is the hottest trend in the global E-Commerce space.

With the use of machine learning algorithms for E-Commerce and the processing of huge amounts of data, you can thoroughly analyze the online activity of hundreds of millions of users.

Companies like Netflix, YouTube, Amazon, etc. use recommendation engines by considering experience of other customers and recommend products that are bought by people who live near the customer, age, gender and so on.

Marketing Campaigns :

Acquiring new customers is much most cost-intensive than retaining existing customers.

A machine learning algorithm can determine which shoppers are the most likely to abandon your site- based on things behaviour such as: returning to your store less frequently, smaller purchases, etc.

Based on the result we can target the customers through incentives, recommendation, emails etc.

Managing Supply and Demand :

If you run a successful online store, you have to monitor your stocks, reorder items, predict trends in demand, coordinate contractors, deal with manufacturers, suppliers, mailing services, and manage your revenues accordingly.

By monitoring all your inventory, and even predicting future trends in supply, demand and even cash flow, you can be sure that with Al, you won't be the airline that overbooks flights or flies empty.

You will be the airline that plans their next year schedule with absolute confidence.

Improved Customer Service :

Chatbots are amongst the most accessible examples of machine learning in ecommerce. These bots help with common queries and direct visitors to specific products.

An Al-enabled bot can use the interactions it has to learn and tweak its future replies. The more a chatbot gets used, then, the more human it seems, and the better the information it provides.

Price Optimization :

Price is one of the major drivers for at least 47% of customers according to Big Commerce survey.

Even the big giants like Amazon, Flipkart, Uber, Airline companies, Airbnb do price optimization.

We can also include other factors like supply and demand, which promotions are running, what time of the day or year it is, shipping location and so on.

Image Processing and Recognition :

Ideally, it enables a customer to just upload a photo they snapped of a given product at home or at the store.

The system then processes it on the store's servers, and instantly displays a response with availability, current price, shipping info so the shopper knows where to buy it.