Deep dives across data, automation & AI

Put Claude's Built-In Skills to Work: The Power User's Field Guide*

Reciprocal Rank Fusion: Merging Multiple Retrieval Results into a Single Ranked List for Hybrid RAG Pipelines

Hybrid RAG pipelines combine keyword and semantic search — but merging their results without breaking both is harder than it sounds. This lesson teaches you Reciprocal Rank Fusion from the formula up through a complete, production-ready Python implementation, so you can build retrievers that outperform either approach alone.

Building a Citation and Source Attribution System for RAG: Linking LLM Responses Back to Source Documents with Confidence Scores

Most RAG systems retrieve documents and generate answers — but never connect the two in a way users can actually trust or verify. This lesson builds a complete citation pipeline that maps every LLM claim back to a specific source chunk, complete with composite confidence scores and hallucination detection.

AI-Assisted Data Cleaning: Using LLMs to Standardize, Deduplicate, and Validate Messy Datasets

Messy data doesn't just waste time — it poisons every analysis downstream. Learn how to build a Python pipeline that uses LLMs for the parts of data cleaning that require real judgment: normalizing company names and job titles, catching semantic duplicates that fuzzy matching misses, and validating records for logical consistency.

Understanding Similarity Search: Cosine, Dot Product, and Euclidean Distance for Vector Retrieval

Before you build a RAG pipeline, you need to understand what "similar" actually means in vector space. This lesson breaks down cosine similarity, dot product, and Euclidean distance from first principles — with working Python code and real retrieval scenarios — so you can make the right choice every time.

Retrieval-Augmented Generation Explained: How RAG Works and When to Use It

LLMs are powerful reasoners, but they can't answer questions about your data — unless you give them a way to look things up. This lesson explains exactly how RAG works, walks you through building a real pipeline in Python, and shows you when to use it versus other approaches.

What Is Generative AI? A Plain-Language Guide for Data and Business Professionals

Generative AI is reshaping how data and business teams work — but most explanations either oversimplify it or drown you in jargon. This guide explains how large language models actually work, where generative AI excels, where it fails, and how to use it intelligently in professional settings.

Guardrails for RAG Pipelines: Implementing Input Validation, Output Filtering, and Policy Enforcement in Production

Most RAG pipelines fail not because retrieval is bad or the LLM is wrong — they fail because nothing validates what goes in, filters what comes out, or enforces organizational policy in between. This lesson teaches you to build production-grade guardrails layer by layer, from prompt injection detection to grounding-based hallucination checks, with a policy engine that lets your compliance team update rules without a deployment.

Implementing LLM Gateway Middleware: Centralized Auth, Rate Limiting, Audit Logging, and Fallback Routing Across Multiple Providers

Direct API calls to multiple LLM providers create fragile, unobservable, ungovernable systems. This lesson walks you through building a production-grade LLM gateway middleware in Python — with JWT auth, Redis-backed token bucket rate limiting, circuit-breaker fallback routing, and async audit logging that doesn't block the critical path.

Semantic Caching and Vector Search for LLM Applications: Reducing Latency and Cost in High-Volume Enterprise AI Pipelines

Exact-match caching is nearly useless for LLM applications because real users rephrase constantly. This deep-dive lesson teaches you how to build a production-grade semantic cache using vector embeddings and similarity search — including threshold tuning, multi-tenancy, cache invalidation, and performance monitoring. By the end, you'll have working code and the engineering intuition to deploy it at scale.

Multimodal RAG: Retrieving and Reasoning Over Text, Tables, and Images in a Unified Pipeline

Most RAG pipelines silently ignore tables, charts, and diagrams — the parts of documents that often contain the most critical data. This lesson builds a complete multimodal RAG pipeline that extracts, indexes, and retrieves text, tables, and images together, then feeds them to a vision-capable LLM for accurate, grounded answers.

Building a Feedback Collection and Human Preference Dataset Pipeline for Continuous LLM Application Improvement

Most LLM applications ship and stagnate. This lesson shows you how to instrument your app to capture user feedback, structure preference pairs for DPO and RLHF workflows, and build a complete pipeline that turns raw signals into training data — so your model actually gets better over time.