How to use AI generation for software development

 Several ways to use GenAI tools to write higher-quality code, making it more efficient and ready for production.


Highlights

Description

Example

Define a Role

Start your prompt by assigning a specific professional role to the AI. This provides context and guides the AI to respond from the correct perspective (e.g., development, architecture, security, QA, DevOps).

“You are a senior Python developer. Write an API to get the current temperature of a city.”

Be Specific

Provide detailed context such as use cases, target users, programming language, frameworks, and coding standards. The more specific the prompt, the more accurate the AI output.

“What are the best Python frameworks for a weather API?” → “Write a FastAPI service to retrieve the current temperature for my city.”

Give Feedback

Use an iterative approach by reviewing AI output, providing feedback, and requesting refinements to improve quality and correctness.

“Update the API to add stricter input validation, improved error handling, and logging.”

Request Expert Opinion

Ask the AI to review your code from different expert perspectives to identify issues and suggest improvements.

“You are a security engineer. Review this API code for vulnerabilities and suggest fixes.”

Iterative Improvement

Combine role definition, specificity, feedback, and expert review to evolve code into a production-ready system.

A Weather API refined by a Senior Developer, Security Engineer, SRE, Test Engineer, and DevOps Engineer is reviewed.

Improve Skills

Continuously upgrade your skills to stay current as AI reshapes software development.

Enroll in DeepLearning.AI’s Generative AI for Software Development course to combine coding skills with LLMs.

Comments

Post a Comment

Popular posts from this blog

Story Points Are Really Simple

Image
  Story Points Are Really Simple Story Points helps Agile teams estimate the effort required to complete tasks by focusing on complexity, size, and uncertainty rather than time. This method is ideal for teams distributed across multiple locations and countries. Why Use Story Points? Reason Detail Assessing Effort - High accuracy: Considers many factors, not just working hours. - Handling uncertainty: Easily adapting to unexpected challenges. Teamwork - Shared understanding: Ensure everyone agrees on the meaning of each point. - Leverage diversity: Different perspectives improve estimates. Better Planning - Track progress: Know the points the team has scored in each sprint. - Work Forecast: Plan sprints based on previous performance. Flexible - Easy Adjustments: Change plans without being constrained by specific time estimates. - Complexity Management: Break down complex tasks into manageable components. Story Score Comparison Table Story Point Fibonacci Points Shirt Size Level of E...
Read more

Comparing Event-Driven Architecture (EDA) and Event Sourcing (ES)

Image
Event-Driven   Architecture ( EDA) and Event Storage (ES) are two different but complementary design models in software development. EDA : Focuses on real-time responsiveness with component decoupled functionality. ES : Provides a complete event history for better status management. Both methods can be integrated to achieve a balanced system design! Comparison Table of Event-Driven Architecture (EDA) and Event Storage (ES) Aspect Event-Based Architecture (EDA) Event Storage (ES) Purpose Real-time event handling Manage historical state changes. Data Processing It reacts to events and can store the resulting state. Record each event in order to reconstruct the state. Main Benefits - Separate the components - Real-time feedback - Scalable - Complete history - Restore state - Auditable For example - E-commerce (orders, payments) - IoT system - Financial transactions - Version control Scalability Easily expandable horizontally. It is scalable, but requires centralized log management. In...
Read more

4 Ways AI Is Redefining What “Senior” Really Means at Work

Image
Have you noticed how two people with the same Senior title can feel worlds apart? One struggles to keep up with new tools. The other adapts calmly, asks better questions, and somehow stays relevant even as technology shifts overnight. The uncomfortable truth is this: AI is quietly redefining what “senior” actually means at work —and it has far less to do with years of experience than it used to. Across software engineering, data, product, analytics, and even management, AI is closing skill gaps at a speed we’ve never seen before. Tasks that once required years of experience and deep tool specialization can now be done in minutes with AI copilots. So if execution is no longer the differentiator, what is? 1. Seniority Is Shifting From “Doing” to “Deciding” For a long time, senior professionals were defined by execution: They could handle complex tasks independently They knew tools inside out They needed minimal supervision Execution was the badge of honor. AI has changed that. Today, AI...
Read more