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I am a Journalist-turned-Software Engineer. I love coding and the associated grind of learning every day. A firm believer in social learning, I owe my dev career to all the tech content creators I have learned from. This is my contribution back to the community.
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This is part 3 of the AI Agent Architecture Series. You can find the previous articles here. In this article, we will talk about the Dataset Alignment Method for creating agent profiles. Dataset Alignment Method The Dataset Alignment Method grounds...
📖 Introduction In modern AI applications, communication between clients and tools isn’t always as simple as calling an API. The Model Context Protocol (MCP) provides a standardized way for clients to exchange information, invoke tools, and maintain ...
If you’re curious about how to build an intelligent agent using Model Context Protocol (MCP), you’re in the right place. In this post, I’ll walk you through how to: Create an MCP Server using FastMCP Expose a tool that calculates BMI Build a Clien...
In our previous tutorial, we built a restaurant customer support chatbot using OpenAI's Agents SDK. In this follow-up, we’ll explore guardrails—a critical feature that enhances AI chatbot safety and reliability. What Are Guardrails in AI Agents? Guar...
Video https://www.youtube.com/watch?v=UPBMkFSJdBI The Problem at Hand Uber's data platform processes approximately 1.2 million interactive queries monthly, with 36% of these coming from the operations organization. This group—comprising engineers...
Video https://www.youtube.com/watch?v=FgRGwnpd2HY Google's recent white paper on "Agents" has created quite a buzz. The paper explores the concept of AI agents and delves into their architecture and potential. Let's break down what this white paper...
I am Zahiruddin Tavargere (Zahere). A social-learner, here to learn, share and grow with the tech community.
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I am Zahiruddin Tavargere (Zahere). A firm believer in social learning, I owe my dev career to all the tech content creators I have learned from - this is my contribution back to the community.
Note: This post has a lot of pictures from the 3-day event hence the longer read time. This is NOT a review of the summit but me simply sharing my notes from my favourite sessions.
I had a company-sponsored opportunity (very grateful) to attend Analytics Vidhya’s 3-day DataHack Summit. The theme for this year was…
This will quickly recap my learnings and takeaways from the 3-day event.
Note: I only attended the sessions I thought were relevant to my work. Below are notes from the top sessions I attended.
The first session I attended was on the theme I am deeply interested in today - AI Agents
Takeaways:
It consists of two basic components:
A planner, which prompts an LLM to generate a multi-step plan to complete a large task.
Executor(s), which accept the user query and a step in the plan and invoke 1 or more tools to complete that task.
Critique & Revise Design Pattern
Based on the paper Reflexion the idea uses a LLM to examine whether the output of another generative model used by the Agent is “on the right track” during generation.
Consider baking a cake. When you mix all the ingredients and put them in the oven, you eventually need to check on the cake.
You might open the oven door, peek inside, and see how it's rising and browning, and make adjustments accordingly.
Finally, when you take it out of the oven, you evaluate the result: Is it golden and fluffy, just as you intended?
If so, you've successfully created what you set out to make. If not, you note what changes to make next time.
I missed the first 20 minutes of this session due to an overlap with the previous one but learned a few gems from one of my favorite GenAI educators Dipanjan Sarkar.
He has open-sourced his entire slide deck here.
The most suitable RAG paradigm for your application hinges on several crucial factors:
Complexity of the task
Domain-specific requirements
Computational resources
Understanding the strengths and limitations of advanced RAG empowers you to make informed choices when building applications that leverage RAG.
By carefully considering these factors, you can harness the power of RAG to create effective and informative applications.
Takeaways
Document intelligence is the use of AI and machine learning techniques to automate the extraction, analysis, and management of data from various types of documents.
This technology is increasingly being integrated into business processes to enhance efficiency, accuracy, and decision-making.
Document intelligence systems leverage Optical Character Recognition (OCR) and machine learning to extract text, key-value pairs, and structured data from documents.
This includes the ability to recognize tables, checkboxes, and other layout features, allowing organizations to convert unstructured data into usable formats quickly and accurately
My biggest takeaway from this session was learning about the open large-scale datasets for document understanding - something I wanted for my experiments.
And learning about Table-GPT and DocLLM.
Topic modeling is a widely utilized technique for uncovering and presenting thematic structures within text data. This method is essential in fields like information retrieval, text mining, modern search systems, and data visualization, enabling researchers to efficiently explore large textual datasets. Over time, various approaches have been developed to enhance the analysis of textual data and uncover hidden themes, including Latent Dirichlet Allocation (LDA), LDA with WordNet, Neural Networks, Linguistic Extensions, BERTTopic, and KeyBERT.
However, before the emergence of large language models (LLMs), topic modeling encountered several challenges:
Lack of labeled data
Limited understanding of context
Difficulty in interpreting topics
Inflexibility
Limited scalability
Absence of pretrained models
In this session, I learned how to leverage LLMs for topic modelling.
Attending the 3-day GenAI summit was an incredibly enriching experience.
Events like this are invaluable for staying at the forefront of emerging technologies, expanding your professional network, and gaining fresh insights from industry leaders.
They provide a unique opportunity to learn about the latest advancements, exchange ideas with like-minded professionals, and explore new ways to apply AI in your work.
Whether you’re looking to deepen your knowledge, collaborate on innovative projects, or simply stay ahead in a rapidly evolving field, attending such summits can be a significant step toward achieving those goals.