AI for Students - Useful AI Tips

AI for Students - Useful AI Tips

 

Understanding Generative AI and Generative AI Platform leaders

We are hearing a lot about power of Generative AI. Generative AI is a vertical of AI that holds the power to #Create content, artwork, code and much more. Numerous studies have shown this transformative capability has led to numerous benefits across sectors. There has been 40% increase in efficiency in content creation, a 75% surge in creative output & an upto 90% growth in the level of automation in certain workflows. It is interesting to study how GAI (Generative AI) is revolutionizing traditional processes and opening doors to innovative possibilities. Generative AI, as you know is a subset of AI that focuses on teaching machines to produce original and creative content. 

While traditionally AI operates based on predetermined rules, Generative AI builds ability to learn from data and generate content autonomously. This technology leverages complex algorithms and neural networks to understand patterns and produce outputs that mimic human-like creativity.

The significance of generative AI lies in its potential to revolutionize industries across the board. From content creation to software development, generative AI tools are paving the way for greater efficiency, creativity, and innovation. Companies are increasingly adopting these tools to streamline their processes, reduce manual efforts, and unlock new possibilities that were once unimaginable.

• Healthcare: In the medical field, generative AI assists in analyzing medical images, Xrays & scans, diagnosing diseases & predicting patient outcomes. Radiologists using generative AI for image analysis reported above 30% improvement in accuracy in detecting subtle anomalies, ultimately leading to more timely and accurate diagnoses.

• Software development : Generative AI is transforming the way developers write code. It aids developers by generating code snippets, improving software testing by identifying approximately 30% more defects & even suggesting optimal solutions to coding challenges. These features result in faster development cycles , reduce redundancy and deliver better code quality.                                                                                                                                        
• Content creation: Writers, marketers, and content creators are utilizing Generative AI to automate content generation, effectively streamlining workflows and achieving a remarkable 40% reduction in time spent on content creation. This efficiency boost allows them to focus on higher-level strategic tasks and creativity                                                       
• Language translation Language barriers are being broken down as generative AI tools translate text and speech in real-time, enabling seamless communication across diverse languages. These tools achieve an amazing 95% accuracy in translation and that is helping foster global collaboration and understanding.                                                                                              
• Gaming : Developers are using generative AI to create immersive virtual worlds, generate in-game content, & adapt gameplay based on player behavior. Some reports hace found that this real-time adaptation is resulting in upto 50% increase in player engagement and satisfaction, enhancing the overall gaming experience.                                                                                                                                    
• Finance: For a long time now institutions are leveraging generative AI to analyze market trends, predict stock movement with an impressive 85% accuracy & optimize trading strategies. This technology-driven approach has led to a 25% increase in trading profitability and more informed investment decisions.                                                                                                 
• Art work & Design:  Artists are exploring generative AI for creating unique visual art, illustrations, and designs, pushing the boundaries of creativity. A study found that incorporating generative AI in the design process led to a remarkable 75% increase in the number of innovative and eye-catching design concepts produced.

• Music Composition: Generative AI tools have extended their capabilities to the realm of music composition. These tools analyze existing musical compositions and generate original melodies, harmonies, and rhythms. Musicians and composers can leverage these tools to break creative barriers and discover new musical ideas.

As generative AI continues to advance, its applications across every industry vertical  are expected to become even more sophisticated, further revolutionizing the way we work, create, and interact across various sectors.

You may want to read about the following platforms that are revolutionizing AI

1.ChatGPT

2.Scribe 

3.AlphaCode

4.Gpt-4

5.Bard

6. Cohere Generate

7.Dall-E2

8.Synthesia

9.Flowmachines

10.Claude -  Multi lingual Code Helper

11.ArtBreeder -  Image Creation

12,AI Dungeon - Storyteller

13. DeepCode - Code Reviewer & Suggestions

14. Duet AI - 

15. PaintChainer - B/W Coloring tool

 

 

 

 

ABC Technical Architect Training

ABC Technical Architect Training

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Overview of my 12 week training plan designed to equip aspiring and current technical architects with the essential knowledge and skills required to design, evaluate, and lead modern software systems. Each week includes theoretical sessions, hands-on labs, and assessments.

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Week 1: Foundations of Software Architecture

• Introduction to Architecture Roles and Responsibilities

• Architectural Styles and Patterns (Monolith, Microservices, Event-Driven, etc.)

• SOLID, DRY, KISS, YAGNI Principles

• Case Study Discussion + Assignment

Week 2: Architecture Design Principles

• Domain-Driven Design (DDD)

• Clean Architecture & Hexagonal Architecture

• Architecture Decision Records (ADRs)

• Group Exercise: Designing a Modular Application

Week 3: Cloud-Native Architecture

• Cloud Service Models (IaaS, PaaS, SaaS)

• Serverless Design & Event-Driven Computing

• 12-Factor Apps

• Lab: Deploying a Serverless App

Week 4: Infrastructure & DevOps for Architects

• Infrastructure as Code (Terraform, AWS CDK)

• CI/CD Pipeline Architecture

• Containerization and Kubernetes Basics

• Lab: Create a CI/CD Pipeline with GitHub Actions

Week 5: Application & Integration Architecture

• API Design (REST, GraphQL, gRPC)

• Event-Driven Design and Messaging Patterns

• API Gateway, Service Mesh (Istio, Linkerd)

• Hands-on: Implementing API Gateway with Rate Limiting

Week 6: Security Architecture

• Threat Modeling & Secure Design Principles

• IAM, Encryption, Zero Trust Architecture

• Compliance (SOC2, GDPR, HIPAA basics)

• Exercise: Securing a Cloud-Native Application

Week 7: Observability & Resilience

• Monitoring, Logging, Tracing (OpenTelemetry, Prometheus)

• Chaos Engineering Concepts

• Lab: Building Resilience with Circuit Breakers & Retries

Week 8: Data & AI Architecture

• Data Modeling and Storage Patterns (OLTP, OLAP, NoSQL)

• Real-Time Data Streaming Architecture (Kafka, Kinesis)

• AI/ML Architecture, MLOps Overview

• Workshop: Designing a Data Pipeline

Week 9: Enterprise & Solution Architecture

• TOGAF, ArchiMate, Zachman Basics

• Business Capability Modeling

• Roadmapping & Portfolio Architecture

• Practice: Drafting a Solution Architecture Document

Week 10: Platform Engineering & Developer Experience

• Internal Developer Platforms (IDPs)

• GitOps, ArgoCD, Feature Flags

• Platform as a Product Mindset

• Exercise: Building a Developer Onboarding Flow

Week 11: Emerging Technologies & Trends

• Edge & IoT Architectures

• Blockchain, Web3, DApps

• Quantum-Resilient Cryptography

• Green Software & Sustainable Architecture

Week 12: Capstone & Review

• Capstone Project Presentation

• Architecture Review Board Simulation

• Soft Skills: Stakeholder Communication, Trade-off Narration

• Final Evaluation & Feedback

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Deliverables:

• Weekly Quizzes & Assignments

• Hands-on Labs & Mini Projects

• Capstone Project

• Certificate of Completion

Recommended Tools:

• Visual: Lucidchart, PlantUML, draw.io

• DevOps: GitHub Actions, Docker, Kubernetes, Terraform

• Cloud: AWS, Azure, GCP (based on organization preference)

• Data: Kafka, PostgreSQL, Redis, Athena

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Optional Tracks (Post Training):

• Specialized Deep Dive: AI Architect, Cloud Solution Architect, Platform Architect

• Certification Preparation: AWS SA Pro, Azure Architect Expert, TOGAF

 

What is Artifical Intelligence infused BPM ?

When I had created Accenture's Point of View (POV) on BPM (comparative study of BPM products or iBPM products as Gartner likes to calls it). We had predicted year on rise of 25% in adoption of BPM by the top 5 industry verticals and year on average productivity improvement of over 10% for enterprises that have implemented matured BPM tools (We had suggested that a mature BPM implementation alone could improve the enterprise productivity by 10%) . Back then Digital Transformation was not a buzz word and we see not just businesses but even governments are aggressively pushing for Digital Transformation. Feels great when I see that today people are well past discussing Need For BPM for Digital Transformation and discussion has moved over to the sensational Artificial Intelligence infused BPM'.
                                                      Artificial intelligence focuses on making already “intelligent” systems capable of simulating human-like decision-making and execution – enabling those systems to perform functions traditionally executed by skilled human professionals – but do so at a much higher level, because of the speed and power available on modern computing platforms.  One needs to understand that for AI TO REALLY HAPPEN the AI software architecture would have to be be similar to our own central nervous system, which controls most of the things we do even though we don’t consciously think about it. So when ever AI matures instead of nerve signals, AI uses algorithms to simulate human behavior.
                                                  Frankly 'what we are implementing today does not have 'human like decision making' capability and that's why we cannot call it AI.  AI is the future and huge investment in research are being done but existing systems do not have intelligence similar to humans because we do not have capability to produce software that has emotional and biochemical aspects of a human brain. What people at large refer to as AI (as of  Jan 2018 ) is actually Machine Learning driven by big data & data mining and which gives insight to improve decision making but there is no Human Like Intelligence as claimed by some companies. Fact remains that the insight from big data aids better and smarter decision making as decision making has definitely improved as we have huge data and technology to process the data at a fast pace.  As such we have been using data insights from historical data to make better business decisions for quite a few years now and if industry decides to call this data insight as AI then we can say AI & BPM are old friends.
                                                                     So if someone tells you he is working on something revolutionary, integrating AI with BPM, you can tell him that AI-BPM is in production for quite sometime - actually for quite a few years (smile)! We did implement Smart Business Process that could be triggered by events from Complex Event Processing framework based on certain event types. We did implement Real Time Big data processing and integrated it with BPM to get insight from Data In Motion and make smarter decisions in real time.  In short we have been doing AI driven BPM for years so don't get stoned by tall claims by some AI-BPM expert!

Point I want to make is that though AI-BPM is not new at the same time AI has been evolving at a fast pace along with ML and we need to continuously  innovate and integrate ML with BPM to get better business insights. What we have already implemented for various industry is a Smart Next Best Action capability that aids a software system to take better decision in real time. Typically NBA is a custom software that uses intelligent insights extracted from big-data processing to aid enterprise decision making and we are using the word intelligent not because system is smart like humans but because it makes decisions based on millions of past records or transactions to recommend the most appropriate action  - something which can almost act like a human not because of intelligence but because of Machine Learning.

Here are some random industry numbers about AI & BPM  -

• As of today more than 50% of the businesses that are processing Big Data have implemented AI solutions and these businesses have reported more than 50% increase in new business insight from Big Data.
• AI has helped 50% of implementations to make better business decisions, 20% businesses claim to improved automated communication with help of AI and only 6% businesses claim to have reduced work force by implementing AI
• Most implemented area for AI is Predictive Analytics (eg Weather Data, Operational Maintenance etc )
• More than 80% of the implementors claim that AI has improved efficiency & created new jobs
• Almost all implementors acknowledge that Data Analytic technologies are more efficient when coupled with AI
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So the intellegence from insight from the huge data is helping make busniness more smart, more proactive, more predictive,  more efficient, more productive and more customer friendly thus opening avenues for new products and expansion of business.

So how is AI or DI changing in the BPM?

1. Intelligent Recommendations - Continuous machine-learning can  provide relevant recommendation to customer as well as business
2. Intelligent Marketing - AI can make recommendations to agents or directly to consumers using profile attributes & response behavior and keep learning in real-time, so that the next best offers are relevant to the customer and keep improving over time. Software can help marketing  agent deliver the right recommendations to the right customer at the right time.
3. Process Automation - Data-insight help reduce workflow inefficiencies, automate human tasks & processes, and reduce repetitive tasks. 
4. Preferential Treatment to Valued Customers - ML and predictive analytics can estimates a customer’s behavior and guide the agent to both satisfy the customer.
5. Next Best Action -  NBA helps agents guiding them about the next-best-action to take that will solve a specific problem and lead to higher customer satisfaction and also predicts the sales lead conversion and reduce customer churn
6. Sales Prediction -  Predictive Analytics helps predicts the likelihood of a lead to close and suggests next best action and strategies to the sales agent.  Predictive engine can identify new sales oppurtunities that may not be outright visible to the team.
7. Customer Retention - Predictive engine can predict customer churn and also suggest steps required to retain customer.      

Game changing BPM & Data Intelligence/ Artificial Intelligence

What BPM can deliver today is not just efficient and smart process management but a real time business management. The BPM game is changed and BPM is now offering

1) Predictive Business  - Analyze, Sense, Learn, Predict, Act
2) Proactive Recommendation leads to better customer service
3) Reduce Churn by predicting and addressing customer concerns
4) Better value to customer value delivered based on customer insight
5) Better Forecasting by 360 degree view of customer and business
6) Real time enterprise proactively addressing real time events

There are many BPM vendors and vendor analysis by Accenture, Gartner or Forrester can help you decide which BPM vendor has product and features that are right to deliver your solution. Pega, Appian are some of the leading BPM layers of 2018 but there are at least 19 BPM vendors to chose from and you can refer to How to select a BPM (Business Process Management) product? to know how to go about selecting the right BPM product 

Tomorrow of every connected enterprise is Hyper-connected Enterprise

 

Hyper-connected Home

Where do buy sugar from? Pay cash and buy from local grocery store? or order sugar on mobile app? If you are using #BigBasket or #Amazon to buy 1kg Sugar then you should be aware that 100s of computers/devices and at least half a dozen companies,from manufacturer to stockist to cargo delivery to Amazon, all are collaborating over a 'hyper connected network' to get your sugar delivered on time. Which means you are already part of Amazon's Hyper-connected Enterprise where computers, devices and employees create a seamless delivery experience for the customer. Your smart-city is becoming a Hyper-connected city - water, electricity, garbage, emergency services, healthcare, social-welfare everything has either moved or moving to Hyper-connected network of enterprises. You may want to check how companies like Libelium World are helping monitor environment in smart-cities in Spain.

Take another example of entertainment industry. How do you watch your favorite sport? In your living room on large screen TV, live stream on your mobile/tablet/laptop or do you use a dedicated app like Hotstar? Most likely you use all these medium to watch a game of live cricket. Entertainment is being delivered today everywhere and anywhere you choose to be. It is possible because #Hotspot and #Accenture have created a Hyper-connected environment to deliver the content on all medium to deliver content even when you have poor network connectivity. Energy sector suffered huge losses because of electricity leakage and was one of the first to adapt Smart Meter & Smart Energy distribution system. A decade back #USA #Florida based Duke Energy claimed that its electrical system have the capacity to automatically detect, isolate, and reroute power when a problem occurs.

 

Your enterprise too is live 24/7 because the customers expect you to provide service not just on phone between 9 to 5 but at a time that is convenient to them and over a medium of their  choice. The epidemic has only accelerated seamless service delivery over digital medium in a transparent, reliable and secure manner. The enterprise has evolved in many ways and new business models of collaborations are in play to deliver services to the consumer.The digital enterprise is evolving to a new paradigm and it is called Hyper-connected Ecosystem.The biggest change that we are witnessing today is not just the mandate for extraordinary agility and business resilience but also a drastic shift in consumer demand. An evolution of the digital ecosystems that are driving businesses today required organizations, people, devices connecting seamlessly by leveraging an effective hyper-connected ecosystem.

Some leaders talk of Hyperconnected Enterprise as the next phase of Digital enterprise what they don't realize is the evolution had already started years back and you are already on a Hyper-connected Network.The core idea behind Digital Enterprise was always to deliver a services over a connected ecosystem and pandemic has  necessitated innovation to make a business impact and ensure sustainability. The focus today is to enable new innovative open ended technology solutions that are seamless and integration ready to reach the masses. 

 

Blocks of Hyper-connected Enterprise

 

Some enterprises may already have a well designed information system that requires minimal work to become hyper-connected enterprise. The remaining enterprises have to redesign the process ,underlying systems and imbibe the culture of digital dexterity.  The enterprise vision has to change along with the employee mindset to adapt and embrace emerging technologies along with existing technologies to achieve better business outcome and to deliver new products. Not an easy order as it requires enterprise and employees to learn new skills as well as change in culture. Every industry is either moving to become a hyper-connected enterprise or they will have to quickly do the transformation if they intend to stay relevant and compete with the competition who have adopted the new way of doing business. 

The journey

Today enterprises need to be connected to deliver value. From lead generation to fulfillment to customer support all the processes have to be digital.In the next post we will discuss some examples of Everywhere Digital Enterprise. Until everything is connected to everything else...

 

The Evolution of Software Integration

 

Every successful enterprises depend s heavily on underlying software applications and communication between the applications. The problem is that, as time goes by, enterprises invariably end up with software created with disparate technologies and built by several vendors.

The number of software applications varies according to the size of the organization. According to a research small businesses use an average of 10 to 22 applications, and in large enterprises, this number rises to an amazing 700 to 1000 software applications!

The Motivation for Software Integration

All these disparate software applications often need to work together, and this is where software integration comes in. I see various motivations for software integration when I talk to business owners and IT managers. They usually want to achieve one of the following:

• Produce a unified set of functionalities, for example, a unified customer support system
• Increase productivity by reducing the need to switch between applications
• Have easier user adoption, especially if one of the software applications being integrated is new
• Enable data analytics by getting data from multiple sources
• Automate data entry – getting data from another application is less costly than manual data entry

In the early stages of software integration, one of the main issues would be that everything was proprietary and closed. You would buy an application, and all the information you put in it was accessible only from within that application. Not to mention that often it would be available on a single machine or on a limited set of machines. If you wanted to, for example, access that information from another software application, you were into trouble.

But when there is a will, there is a way, and so software integration started. The software integration challenges were initially addressed by implementing in-house integration solutions that used ad hoc and proprietary protocols themselves.

Eventually, with the addition of more and more software systems and the wider spread of internal and external networks, home-grown solutions were no longer adequate. Software integration evolution had reached a new level. The motto "no system is an island" became common, but there was still no standard solution to the problem.

Software Integration Evolution & APIs

Over several decades, enterprise integration technologies leveraged one or more integration styles. The earliest integration style was the Remote Procedure Call (RPC) style, used in single-language environments and very popular in the 80s and early 90s. Later, multi-language systems such as CORBA appeared. In these, an explicit Interface Definition Language (IDL) was used, so that the caller and callee could be implemented in different programming languages and even different platforms.

In the end, the use of Application Programming Interfaces, best known as APIs, became the rule. APIs emerged to expose business functionalities provided by one application to other applications. APIs exist so you can reuse them – the concept has been, from the beginning, that multiple applications could consume the same API. The idea was that developers could rely solely on the API to access an application's functionality without worrying about their implementation details.

What Is API Management & Why You Need It

When developers start using an API, they hope that the API is a contract that will not change. However, APIs are susceptible to the same environmental pressures to change that all software systems face. They are often upgraded, reworked, and sometimes refactored. When this happens, finding actual points of change in the API and making things work again is painstaking work for the developer. This is why API lifecycle management appeared.

Developers also hope they will have to work with as few APIs as possible. This is because each new API represents a new learning curve and involves time and effort. Moreover, when you come across the upgrade problems we mentioned, the developer knows it will help to have few APIs and few inter-dependencies - not to fall into spag

The thing is that this is not always up to the developer, as the need to integrate different software systems grows.

This is what API management is: API lifecycle management for multiple APIs.

As a result of sheer demand, API management using middleware has emerged as a way of using APIs and getting their advantages, while avoiding their known problems. Please note we are looking at API management from the API consumer perspective. If you look at it as an API producer, then the focus will be different.

API Management Tools As Integration Software Solutions - An Example

The new middleware technologies have eliminated the need to call APIs directly. Instead, the developer writes SQL in their new or legacy code, then use prebuilt connectors to translate standard SQL syntax in that code into API calls. These calls retrieve the needed information from the target system (4). This can work for retrieving data (SELECT) or to input/change it (INSERT, UPDATE, DELETE).

The middleware acts as a translator that speaks all the API variants the developer needs, translating them into ANSI standard SQL syntax that the developer knows well and can use together with his favorite programming language, such as Python, Java, or C#, just to name a few.

By using such translating middleware, the developer no longer needs to learn a new programming language or gain expertise in the target system API. This makes all the difference, dramatically reducing the time and effort necessary to integrate software.

Using SQL Connector, the developer has two options:

• he can build his own custom integration software in the programming language of his choice or
• he can start from the source code of any software from the past 40 years.

In both cases, completing the integration will require few lines of code and be quite straightforward.

Using such middleware also eliminates the need to redo your code when you upgrade the target system or its APIs. The middleware company itself will handle all the maintenance efforts. It is now their job to guarantee forward compatibility (and sometimes backward compatibility too).

Ultimately, API management gives enterprises greater flexibility by enabling them to go for the software integrations they need while shielding them from the negative aspects and not compromising on security, e.g. maintaining GDPR compliance.

Last word

Software integration has long been a pain point for businesses, often leading companies to either maintain their legacy systems for longer than they should or fork over large sums of money on developers to migrate to the latest and greatest.

Fortunately, with software integration evolution, you can easily solve current integration challenges and prepare companies for the future by using today's technology of API management middleware. Whether to simply share data between systems, to modernize legacy systems, or to meet complex requirements, endless integration possibilities are at your fingertips once you start using API middleware.

Learn Prompt Engineering with ChatGPT & Google Gemini : A Simple Guide with Examples

In the era of generative AI, prompt engineering has emerged as one of the most essential skills for effectively interacting with AI.  For beginners 3 popular AI tools are 

1) ChatGPT (by OpenAI) 

2) Gemini (Google's)  and  

3) Claude (Anthropic) and 100+ other similar AI tools

So how does this Artificial Intelligence really work?

1) You know a computer runs a Software and can Search Internet and store data

2) 10000 computers can Search Internet and store data much faster than 1 computer

3) If I tell computer to search data about about Dolphin or Coffee it can search and store all information and when I ask a question it can then reply to any question related to Dolphin or Coffee in seconds

4)  That is how AI works. There are thousands of computers, all connected to each other,  searching information about 'certain words' and storing it in a Database so that when you ask a question they can reply in seconds. (You are right! That is tremendous waste of resources & electricity and that is contributing to global warming but I will write about it in another post)

5)  Prompt Engineering is a way of writing a command so that computer knows what 'exactly you are looking for' and give you best results and not a generic reply as you get in Google Search.

6) If I want to explain how Coffee is made to a 10yrs old I should tell computer in so many words so he gives an answer with examples so that 10yrs old can understand it

7) If I want computer to explain 30yrs old how to 'Brew Coffee' at home then I should write a command so that I get  a relevant answer 

8) Just understand that the more context you provide to AI, the better reply you will get from AI 

Even before you start reading you should know the answer to following basic questions 

1. AI is fantastic but can a layman talk to these AI tools & get a good reply?

2. Is there any use of Artificial Intelligence in life of a wife, mother, student, lawyer, doctor, chef?

3. Can I start using AI from today?

4. I am 79yrs old, can AI help me in any way and can AI learn to use AI? 

Answer to all the above questions is YES

Prompt engineering is the technique of writing questions (prompts) to get desired outputs from AI systems or in simpler words. Prompt Engineering is way of writing enough details in your question to AI to get best results. This guide is aimed at beginners who are curious about prompt engineering, offering a comprehensive overview of the fundamentals, techniques, and practical examples. If you want to read a 'Marathi' version of this blog please click on this link प्रॉम्प्ट इंजिनिअरिंग: सविस्तर मार्गदर्शक (उदाहरणांसह)

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What is Prompt Engineering?

Prompt engineering tells you what details you should put in your question to AI in a way that gives the most useful, relevant, and accurate results. Because ChatGPT generate responses based on patterns learned from massive datasets, the way you ask a question can significantly influence the answer.

In essence, prompt engineering is about:

• Understanding how ChatGPT or Gemini interpret and respond to input.

• Designing prompts to guide the model's behavior.

• Iterating and refining prompts to improve outcomes.

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Why Prompt Engineering Matters

AI models are highly capable, but they are not mind readers. They depend entirely on the text provided. Subtle variations in phrasing, tone, specificity, or structure can change the results dramatically.

Benefits of good prompt engineering include:

• More accurate and relevant outputs.

• Reduced hallucinations or fabricated content.

• Increased efficiency in achieving results.

• Better alignment with business, educational, or creative goals.

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Basic Principles of Prompt Engineering

1. Clarity

   • Clear prompts produce clearer responses.

   • Avoid ambiguity.

2. Specificity

   • The more specific the prompt, the better the output.

   • Specify the format, tone, length, or point of view if needed.

3. Contextualization

   • Provide background or context to help the model generate more informed responses.

4. Instructional Language

   • Use imperative or guiding language: "List", "Summarize", "Compare", etc.

5. Iteration

   • Refine and reword prompts based on outputs.

   • Use feedback loops.

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Types of Prompts - 

By the way, if you want you can open https://chatgpt.com, register with Google id and get started  using these ready made prompts (blue text)

1. Descriptive Prompts

   • Example: "Describe the atmosphere of Mars."

     Example: "Describe climate at Hawaii in September 2026 to plan a family holiday " 

2. Instructional Prompts

   • Example: "Explain how a blockchain works in simple terms."

     Example: "Explain how a Aeroplane works in simple terms in 2 paragraphs."    

3. Creative Prompts

   • Example: "Write a poem in Marathi language about a 10yrs old girl enjoying rains."

4. Comparative Prompts

   • Example: "Compare the economic policies of USA and India in a tabular format."

     Example: "Explain why per capita income of Srilanka is more than India when India's GDP is much higher than Srilanka." 

5. Conversational Prompts

   • Example: "Pretend you're a tour guide in ancient Rome. Walk me through a day in the city."

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Common Techniques in Prompt Engineering

1. Zero-Shot Prompting

   • Asking the model to perform a task without providing examples.

   • Example: "Translate this sentence into French: 'The sky is blue.'"

2. Few-Shot Prompting

   • Providing a few examples to guide the model.

   • Translate the following sentences to French:  1. The apple is red. 

     -> La pomme est rouge. 

3. Chain-of-Thought Prompting

   • Encouraging the model to reason step by step.

   • Example: "If there are 3 apples and you take away 2, how many are left? Explain your reasoning."

4. Role-based Prompting

   • Asking the model to adopt a specific role or persona.

   • Example: "Act as a professional career coach and give resume tips."

     Example: "As a doctor with 10yrs experience analyze attached CBC report of a CML patient , compare it with reports of patient with same age and provide a summary" 

     ( You will have to attach a image or pdf of the report for above prompt using + sign next to typing area in ChatGPT ) 

     

5. Prompt Templates

   • Predefined prompt formats to standardize input.

   • Templates are useful in automation and large-scale tasks for example running a advertisement campaign or sending mails to large number of invitee etc 

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Tips and Best Practices

1. Be Iterative

   • Start simple and refine as needed.

2. Use Constraints

   • Limit word count, specify format (e.g., bullet points), or define tone (e.g., formal, friendly).

3. Test for Edge Cases

   • See how the model responds to unexpected inputs.

4. Break Down Complex Tasks

   • Use a series of prompts for step-by-step tasks.

5. Utilize System Messages (if supported)

   • Many APIs allow for system-level instructions to guide behavior consistently.

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Examples of Effective Prompting

1. Basic to Advanced Prompting

   • Basic: "Tell me about Newton's laws."

   • Better: "Summarize Newton's three laws of motion in simple language for a 10-year-old."

2. Formatting Output

   • Prompt: "List the benefits of solar energy in bullet points."

3. Using Roles

   • Prompt: "You are a chef. Give me a quick, healthy dinner recipe using spinach and chickpeas."

4. Creative Prompting

   • Prompt: "Write a short science fiction story about AI taking over Mars colonies."

5. Chained Reasoning

   • Prompt: "Solve this math problem step-by-step: What is 25% of 240?"

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Challenges in Prompt Engineering

1. Ambiguity in Prompts

   • Unclear inputs lead to unpredictable outputs.

2. Hallucinations

   • Models may generate false or fabricated information.

3. Token Limitations

   • Each model has a maximum context window (measured in tokens).

4. Bias and Ethics

   • Outputs can reflect biases present in training data.

5. Consistency

   • Responses may vary between runs even with the same prompt.

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Applications of Prompt Engineering

1. Software Development

   • Code generation, debugging, documentation.

2. Marketing

   • Ad copy, email campaigns, content ideas.

3. Education

   • Personalized tutoring, lesson planning, quiz generation.

4. Research

   • Summarizing papers, generating hypotheses.

5. Creative Arts

   • Poetry, storytelling, idea generation.

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Future of Prompt Engineering

As AI models grow more sophisticated, the role of prompt engineering will evolve. The future may include:

• Prompt programming languages: Tools or DSLs for structured prompting.

• Multi-modal prompting: Integrating text with image, audio, or video inputs.

• Automated prompt optimization: AI optimizing prompts for best results.

• Embedded prompt layers: Built into apps and workflows seamlessly.

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Conclusion

Prompt engineering is the bridge between human intent and machine response. It's a powerful tool that unlocks the potential of AI, enabling users to tailor outputs to their specific needs. By understanding the fundamentals, practicing different techniques, and learning through iteration, anyone can become proficient in this modern skill.

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Useful links

• AI Prompt Engineering Theory Ajay's Prompt Engineering in AI  

• प्रॉम्प्ट इंजिनिअरिंग: सविस्तर मार्गदर्शक (उदाहरणांसह) Marathi  

• OpenAI Cookbook: https://github.com/openai/openai-cookbook
  

• Awesome Prompt Engineering: https://github.com/promptslab/awesome-prompt-engineering

• Prompt Engineering Guide: https://www.promptingguide.ai/

AI Starter Kit for Class 10 Students

• How to use AI for better planning of studies? Let's assume your name is Ishaan. Do you know AI can interact with you and find out what is Ishaan's learning style and explain any answer customized to you? Do you know AI knows how best teachers in the world teach so if you ask a question and ask AI to answer the question as world best teacher with 0years experience then AI will actually search the internet and find how worlds best teachers explain answer to a 14 yrs old students with examples that are fun, interesting and easy to remember. Anyway I am sharing  20 ready-to-use prompts across all board subjects (Maths, Science, History, Geography, English, Marathi, Exam Strategy, Motivation) along with  Clear instructions on how to use them

• These examples are designed so student can just copy-paste into ChatGPT (or Gemini/Perplexity) to start using AI. Once you complete this you will be able to write prompts yourself

  What is a Prompt? If you do not know what is prompt or Prompt Enginnering click on this linke for a simple explanation.

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📐 Maths (5 Prompts)

1. “Explain Pythagoras theorem in 5 steps with a cricket ground example.”

2. “Create 10 practice sums for Trigonometry (Class 10 SSC level) with step-by-step solutions.”

3. “Show me 3 common mistakes students make in Probability and how to avoid them.”

4. “Make a quick formula chart for Geometry (Class 10) with examples.”

5. “Solve this problem step by step and explain in simple words: [paste question].”

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🔬 Science (5 Prompts)

1. “Explain Newton’s Laws of Motion with cricket ball examples.”

2. “Summarize the chapter ‘Heredity and Evolution’ in 10 bullet points for revision.”

3. “Create 20 flashcards for Chemistry formulas and reactions.”

4. “Explain the human digestive system using Indian foods like rice, dosa, dal.”

5. “Make 5 multiple-choice questions from the chapter ‘Periodic Classification of Elements’ with answers hidden.”

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📖 History & Political Science (4 Prompts)

1. “Summarize the 1857 Revolt in 10 key points for fast revision.”

2. “Explain Gandhiji’s role in Freedom Movement in story format, easy for board exam.”

3. “Make a timeline of important events in Indian National Movement (1885–1947).”

4. “Give me 5 short notes on Political Science topics from Class 10 SSC syllabus.”

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🌍 Geography (3 Prompts)

1. “Explain Indian Monsoon in simple story format with diagrams.”

2. “Create 10 practice map questions for Geography (Maharashtra SSC) with answers hidden.”

3. “Summarize Agriculture in India chapter in 8 bullet points.”

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📝 English & Marathi (2 Prompts)

1. “Check my essay for grammar and rewrite it in better words: [paste essay].”

2. “Give me 10 practice questions for English Letter Writing (SSC Board style) with model answers.”

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📅 Exam Strategy & Motivation (1 + 1 Prompt)

1. “Make a 2-week revision timetable for SSC Board exams covering Maths, Science, History, and Geography.”

2. “Give me 5 motivational quotes before exam, like Sachin Tendulkar encouraging me.”

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✅ How to Use This Kit

1. Pick a subject you want help with.

2. Copy-paste the prompt into ChatGPT (or Gemini, Perplexity).

3. Read the answer carefully → Use it as guide, not replacement for textbook.

4. Ask follow-up questions if something is unclear.

5. Save good answers as notes for revision.

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