TLDR;

AI tools are no longer just buzzwords but practical assets that can significantly enhance our daily work. Whether you’re converting hand-drawn diagrams, analyzing large datasets, streamlining documentation, or accelerating code development, the key lies in understanding how to apply these tools effectively. The goal isn’t to replace human expertise but to augment it, allowing us to focus on more strategic and creative aspects of our work. As AI continues to evolve, those who learn to leverage these tools intelligently will have a significant advantage in their professional journey. Start small, experiment with these applications, and gradually integrate them into your workflow for maximum benefit.

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Why?

In the rapidly evolving landscape of technology, Artificial Intelligence (AI) stands out as a transformative force reshaping industries, enhancing productivity, and simplifying our daily lives. I’ve witnessed firsthand the profound impact AI can have when strategically integrated into business operations and personal routines. In this article, I aim to articulate how we can leverage AI to make our lives easier, both professionally and personally.

Well, I am not here to tell you about what are the benefits of AI, a lot of people has done pretty good job educating on that topic, I would rather focus on how you can use it to your advantage.

Leverage Artificial Intelligence

After all a tool is good only if you know how to wield it right? In those regards, I personally think that the Age of AI is rising and those who knows how to wield the tools will be the ones who survives. This does not mean that one needs to understand how the stuff works (after all it is boring mathematics right?). We have to understand the practical benefits of the AI.

With that regard, let me explain certain use cases where AI can be useful for tasks, which actually improves efficiency of person yielding better results for organizations and/or for personal use cases.

Coding Assistants

I do not think AI is going to replace engineers anytime sooner, instead right now it is making engineers faster. Engineers leverages tools such as GitHub Copilot and Cursor.ai to help them code faster.

These assistants offers them suggestions to the codes as they write, helping them code faster. They also help out in debugging codes, scaffold code from the scratch, document the code, write tests etc.

Transform Drawings into Diagrams

This one is little “off the road” use case, but it has served me well through the time. Imagine this: You are discussing with your team regarding some flow chart. You draw the chart on paper denoting nodes and the edges. Once the meeting finishes, you ask one of your team member to convert that page into a functioning diagram for your documentation.

Well, not anymore. What I do is snap the picture of that page, give it to ChatGPT or Claude, ask it to convert this picture into diagram and voila! Saves a lot of hours.

Analyze data

When you have to analyze the results of let’s say a survey from across the team, or feedback of clients and you have 100s of entries in them what do you do to analyze them? You need a crisp and precise analysis of the data and summarize it efficiently. Instead of giving it to someone and asking him/her to analyze all the responses, upload the Excel or PDF file to ChatGPT or Claude, ask them to read all the responses and summarize.

Interactive chat bots for Q&A

As project grows larger the documentation also grows larger (at lest it should be right?). What if anytime you want to figure out something specific from the documentation instead of searching through pages, you can ask someone in Chat and they would respond with solution to your problem, pointing you to specific page in documentation as well?

These can be done using RAG (Retrieval Augmented Generation) method. It basically generates vector embeddings of your dataset stores it in a vector database. When you ask a question, it searches for the closest matching words in the dataset, passes the found text as “context” to LLM and generates answer for user’s query. RAG is widely used methodology across the field of AI for various tasks.

… and More

These are some of the ways I use the AI tools to enhance my productivities. There are plenty more tools and tricks that I use as well as are available in the market. Explore, experiment and see what satisfies your needs and helps you work faster.