RAG Indexing, Part 2: Embeddings and the Chroma Vector Store
Embeddings turn text into coordinates and a vector store makes them searchable. This post builds the store, persists it, and shows exactly what Chroma holds inside.
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View allRAG Indexing, Part 1: Loading Documents and Splitting Them Properly
Loading a PDF is easy; splitting it well is where RAG quality is decided. This post covers loaders, chunk size and overlap, and a splitter that silently does nothing.
RAG Explained: Giving a Model Access to Documents It Has Never Seen
A model cannot answer from documents it was never trained on. This post explains what RAG is, how its two paths work, and when fine-tuning is the wrong answer to it.
LCEL Part 2: Chaining Chains, RunnableParallel, RunnableLambda and @chain
A straight line is the easy case. This post covers piping chains together, fanning out with RunnableParallel, wrapping your own functions, and drawing what you built.
LCEL Part 1: The Pipe Operator, Batching, Streaming and the Runnable Protocol
A single pipe operator turns three objects into a pipeline that batches, streams and runs async for free. Here is how, plus the step that silently breaks streaming.
Structured Output: with_structured_output and When You Still Need a Parser
Free text is unusable in a pipeline. This post covers getting validated objects back with Pydantic, and the narrower cases where an output parser is still the right tool.
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AI & Tools
9 min read
RAG Indexing, Part 2: Embeddings and the Chroma Vector Store
Embeddings turn text into coordinates and a vector store makes them searchable. This post builds the store, persists it, and shows exactly what Chroma holds inside.
AI & Tools
11 min read
RAG Indexing, Part 1: Loading Documents and Splitting Them Properly
Loading a PDF is easy; splitting it well is where RAG quality is decided. This post covers loaders, chunk size and overlap, and a splitter that silently does nothing.
AI & Tools
9 min read
RAG Explained: Giving a Model Access to Documents It Has Never Seen
A model cannot answer from documents it was never trained on. This post explains what RAG is, how its two paths work, and when fine-tuning is the wrong answer to it.
AI & Tools
9 min read
LCEL Part 2: Chaining Chains, RunnableParallel, RunnableLambda and @chain
A straight line is the easy case. This post covers piping chains together, fanning out with RunnableParallel, wrapping your own functions, and drawing what you built.
AI & Tools
8 min read
LCEL Part 1: The Pipe Operator, Batching, Streaming and the Runnable Protocol
A single pipe operator turns three objects into a pipeline that batches, streams and runs async for free. Here is how, plus the step that silently breaks streaming.
AI & Tools
16 min read
Structured Output: with_structured_output and When You Still Need a Parser
Free text is unusable in a pipeline. This post covers getting validated objects back with Pydantic, and the narrower cases where an output parser is still the right tool.
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Completed
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LangChain Fundamentals: From the OpenAI API to Your First RAG App
Eight posts taking LangChain from first principles to a working RAG pipeline — token costs, the raw OpenAI API, messages, prompt templates, structured output and LCEL.
Oct 2026 – Oct 2026
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Algorithm Interview 2026: Data Structures — Know Your Tools
Master the 6 data structures that appear in 80% of technical interviews. Each post goes deep on one structure — how it works under the hood, every interview pattern it unlocks, and the exact templates that win offers.
Mar 2026 – Mar 2026
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Part 7 — Routing, Hooks, and Build Optimizations (Vite/Webpack)
Learn how to structure routing, master custom hooks, and optimize React builds using Vite and Webpack for production-grade apps.
Jan 2026 – Feb 2026
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Part 0 — Web Fundamentals for Modern Full-Stack
Build a rock-solid foundation in HTML, CSS, JavaScript, and modern tooling to prepare for a professional full-stack journey using React, NestJS, and PostgreSQL.
Nov 2025 – Nov 2025
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