Key Takeaways from Yesterday's Session

Yesterday's Decoding Data Science session was another reminder that building AI starts with solving the right problem—not just using AI because it's available.

One of my biggest takeaways was learning how to identify the right AI solution:

✅ Automation for repetitive tasks

✅ LLMs for understanding and generating unstructured content

✅ RAG when knowledge retrieval is the challenge

✅ AI Agents for multi-step workflows and decision-making

I also learnt about the 6-Layer AI Application Stack, which provides a practical roadmap for building AI applications:

1️⃣ Problem & Use Case

2️⃣ Data Layer

3️⃣ Intelligence Layer

4️⃣ Workflow Layer

5️⃣ Interface Layer

6️⃣ Evaluation Layer

This session helped me understand that great AI products are built by carefully designing every layer—not by simply connecting to an LLM.

Thank you, Mohammad Arshad and the Decoding Data Science community, for another valuable learning experience. Every session continues to strengthen my understanding of AI engineering and how to build solutions that solve real-world problems.

Looking forward to applying these concepts in my own AI projects!

#DecodingDataScience #ArtificialIntelligence #AIEngineering #LLM #RAG #AIAgents #Automation #MachineLearning #BuildInPublic #ContinuousLearning

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Key Takeaways from Yesterday's Session