This week’s Topic Finder & Ranker classwork led me to an important realization. LLMs are excellent writers. But they shouldn’t be the ones deciding what to say. I extended the Week 4 build into an end-to-end content engine that uses an LLM as an executor, not a decision-maker. What’s different from normal GenAI usage vs this Content writing engine? When we use ChatGPT or any other GenAI tool directly, we usually ask: - “Write a post on X” - “Make it engaging” - “Add a hook” All strategic decisions are implicit and unstable. In this system, those decisions are explicit and frozen upstream. How the system works: 1️⃣ Topic Finder / Ranker: Only topics with real “why now” signals enter the system. No random prompts. 2️⃣ Idea Classification (Editor Brain) - Before writing, the system decides: - What kind of thinking this is - The intent (authority, education, engagement) - How safe or sharp the angle should be. This prevents format and intent confusion later. 3️⃣ Packaging Strategy (Creative Director Brain) Format is chosen before content: - Carousel vs text - Slide count - Visual role per slide. The LLM does not “guess” formatting. 4️⃣ Execution Engine - Only after all decisions are locked: - Post is written - Carousel blueprint is generated - PPT is auto-created via Python and Carousel prompt is generated for non-technical users. Key learning The value isn’t the writing. The value is structuring judgment around the LLM. This build helped me understand the difference between: - Prompting better vs - Designing systems that think consistently Still learning and iterating. Sharing mainly to document how my understanding of automation is evolving.

This week pushed me beyond simply following steps into actually understanding how data flows through an AI automation. Replacing JavaScript nodes with LLM chains sounded straightforward initially. In practice, I learned that LLMs silently break implicit assumptions unless context, schemas, and validation are handled explicitly. I ran into multiple dead ends like rate limits, broken loops, partial logs, and at first it felt like I was doing something wrong. Over time, I realized those failures were exposing gaps in how I was thinking about guardrails and observability. By the end, I was able to replace the JS ranker with an LLM-based ranker, add post-LLM validation, and build a more robust topic-finding workflow. Biggest takeaway: building AI workflows is less about prompts and more about protecting data flow and designing for failure.

Hi team Please find the Week 3 Problem Decomposition Assignment. I worked on a Reddit Reply Assistant use case. Apologies for the late submission. Link: https://docs.google.com/document/d/1aOfobSbNBuOsRIOCnd2kxBXh14fYMgX4IIXM7QuCWFE/edit?usp=sharing @Eila Qureshi Would appreciate a review when you have time. Thanks! Happy holidays, everyone!💥

Hi everyone, This is my Week 3 assignment on Problem Decomposition.For this task, I have chosen the topic “Social Media Workflow – Blog to Instagram Post”, where I broke down the complete process into clear, structured steps following the decomposition framework taught in Week 3. I’ve defined the final output, listed all messy steps, distilled them into minimal viable steps, assigned node types, added acceptance checks, and designed the complete task graph. Here is the document link: 👉 https://bit.ly/3MxotxG @Eila Qureshi , I’d really appreciate your feedback on this assignment. Thank you!