100 Days of Langchain

I’ve been staring at my code editor for the last hour, wrestling with my own laziness. Procrastination is sneaky—it whispers, “You’ll get to it tomorrow,” until suddenly it’s weeks later and you’ve barely scratched the surface of that next big idea. Meanwhile, I watch in awe as people around me build mind bending LangChain agents: PDF-digesting researchers, auto-translating support bots, even “AI DJ” chains that remix lyrics on the fly. These folks don’t hunt for opportunities opportunities hunt for them.

I want that too. Not for some hollow resume bullet, but so that I become the person I’ve been dreaming of: someone who truly understands LLM orchestration, whose expertise clients, collaborators, and my future self can rely on. So here we go—no more excuses:

Challenge: Solve one LangChain problem each day.
Deliverable: A short blog post, shared publicly, explaining exactly what I did, why it mattered, and how it worked.
Focus: Learning pipelines—no feature creep, no polished end-products. Just pure discovery documented.

By Day 100—just over three months—I’ll have:

1. 100 bite-sized LangChain mini-projects in my public portfolio.
2. Muscle memory across every core module: prompt templates, chains, agents, retrieval, memory, function calling, tooling, and more.
3. A habit locked in: show up and ship something, every single day, no matter how imperfect.

What’s the Plan, Bro?

• Daily Problem: From Day 1’s “one-step summary chain” to Day 100’s “RL-driven prompt tuner,” each post solves a narrowly scoped LangChain challenge.
• Public Accountability: I’ll publish on my website and GitHub.
• Beginner’s Mind: I’ll write as though I’m explaining to a smart friend who’s new to LLMs. No “because it’s obvious”—just clear analogies and personal anecdotes.
• Reflection Over Perfection: If I stumble, I’ll share the stumble. If my chain blows up, I’ll debug in public. Every misstep is a waypoint on the map.

A Tiny Mental Model to Carry Forward

LangChain = pipelines of specialists.
Imagine an assembly line (🛠️) where each station—“summarizer,” “translator,” “calculator,” “retriever”—does one job brilliantly, then passes its output downstream. Your code becomes the conductor, not the lone magician casting ad-hoc spells.

Hold that image as we begin. Tomorrow morning (or late night…), I’ll publish Day 1: One‑Step Summary Chain – Build a basic LLMChain that takes an article and outputs a one‑sentence summary. My first attempt at wiring an LLM and a prompt template into a reusable pipeline. By the end of that post, you’ll have code you can copy, tweak, and build on.

Takeaway

Habit beats talent when talent sleeps in. Learning daily—especially in public—is daunting. But if I don’t start now, I’ll never bridge the gap between where I am and where I want to be. So here’s my promise, to you and my future self: I’ll show up every day, even if I only write one sentence of code. Because learning isn’t a checkbox—it’s a journey through curiosity, failure, and small wins.

Ready to see how raw curiosity turns into real expertise? Let’s walk down this pipeline—together. Tomorrow, Day 1: see you on the other side.