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The entry-level AI market in 2026 is wild. Expectations for freshers are skyrocketing, and we’re actively competing against devs with 2 years of industry experience just for a foot in the door. Like many others, I fell hard into the tutorial loop—watching videos, reading docs, and building isolated scripts. But tutorial knowledge completely crumbles when an interviewer asks you to share your screen and build something from scratch. I got tired of the passive loop and the silent applications. So, I’m changing my approach. I’m done consuming; I’m building in public. I’m working on an end-to-end AI Research Analyst Agent designed to break away from linear tutorial pipelines. I'm actively exploring: Parent-Child Chunking for ultra-precise data retrieval. LangGraph to build cyclic self-correction loops (grading hallucinations). PostgreSQL side-by-side with ChromaDB to persist the agent's memory state mid-loop. I don’t know every line of code by memory yet, but I’m learning by building. Check out my full architectural breakdown and raw journey on Medium: https://medium.com/@khandalejuee/ai-research-analyst-agent-agentic-rag-building-beyond-tutorials-part-1-78ed62e59e41 #AI #GenerativeAI #Python #LangGraph #PostgreSQL #BuildInPublic

Juee Khandale

Generative AI, LLM, Full-Stack developer