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LangChain Explained For Beginners
A practical developer breakdown of memory, tools, RAG, prompt templates, LCEL, and why LangChain matters when your AI app grows beyond one…
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20 AI Concepts Every Developer Should Understand Before Building AI Apps
A practical developer’s guide to LLMs, RAG, vector databases, MCP, agents, fine-tuning, and why “just calling an AI API” is only the…
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Day 26: Zero-Shot vs Few-Shot Prompting Explained
A practical developer-focused guide to when a direct prompt is enough, when examples matter, and why few-shot prompting is not the same as…
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Day 25: Building an AI Portfolio That Gets Interviews
Most AI portfolios fail because they show projects. The good ones show judgment, debugging, tradeoffs, and proof that you can build useful…
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Day 24: Fine-Tuning vs Prompt Engineering
How to choose between better prompts, better data, and a custom model when building real AI apps.
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Day 23 of becoming an AI developer: How AI Systems Are Actually Designed in Production
The architecture behind reliable AI apps, RAG systems, agents, guardrails, evals, and the boring engineering that makes AI usable in real…
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Day 22 of Becoming an AI Developer: “ChatGPT vs Claude vs Gemini vs Perplexity”
A practical guide to choosing the right AI tool for coding, debugging, research, architecture, and shipping real projects
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Day 21 of Becoming an AI Developer: AI Workflows That Save Me Hours as a Developer
Practical AI workflows for planning, coding agents, PR reviews, debugging, tests, and production guardrails
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Day 20 of Becoming an AI: Multi-Agent Systems Explained For Beginners
Planner vs Executor, explained through the way real AI apps actually break
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Day 19 of Becoming an AI Developer: Function Calling vs Tool Calling
How weather apps, APIs, and real tools help LLMs move from “I think” to “I checked.”
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