Articles
Explore in-depth tutorials and guides across data analytics, automation, and AI. Filter by topic or difficulty to find exactly what you need.
AI & Machine Learning⚡ Practitioner
Contextual Compression in RAG: Filtering and Compressing Retrieved Chunks Before Passing to the LLM
Raw vector search retrieves the right documents but passes too much noise to your LLM. This lesson teaches you how to build compression pipelines that strip retrieved chunks down to only what's actually relevant to the query — reducing token costs and improving answer quality at the same time.
23 min read
AI & Machine Learning⚡ Practitioner
Multimodal LLM Integration: Processing Images, PDFs, and Documents with Vision APIs
Text-only LLMs can't handle scanned receipts, chart-heavy reports, or complex PDF tables — but multimodal models can. Learn how to build a production document intelligence pipeline that routes, processes, and extracts structured data from any document type using vision APIs, with full cost and accuracy control.
24 min read
AI & Machine Learning⚡ Practitioner
Few-Shot and Zero-Shot Prompting: When and How to Use Examples to Improve AI Output Quality
Most AI prompts underperform because they rely on instructions when they should be using examples. This lesson teaches you the mechanics of few-shot prompting, how to design example sets strategically, and how to debug the most common failure modes — with complete, production-ready prompts for data workflows.
20 min read
AI & Machine Learning🌱 Foundation
Query Routing in RAG: How to Direct Questions to the Right Data Source or Retrieval Strategy
Most RAG systems have more than one data source — and blindly searching all of them for every question is a recipe for slow, noisy, expensive answers. Learn how to build an intelligent query router that sends each question exactly where it needs to go.
17 min read
AI & Machine Learning🌱 Foundation
Handling LLM Errors, Timeouts, and Rate Limits in Python Applications
LLM APIs fail in predictable ways — but only if you know what to look for. This lesson teaches you how to build Python applications that handle rate limits, timeouts, and transient errors gracefully, with retry logic, proactive throttling, and real fallback strategies.
16 min read
AI & Machine Learning🌱 Foundation
Writing Clear AI Instructions: How to Communicate Your Intent So AI Tools Deliver Useful Results
Most AI tools aren't failing you — your instructions are failing them. Learn the four-component framework for writing prompts that consistently get you useful, accurate, and properly formatted results from any AI tool.
15 min read
AI & Machine Learning🔥 Expert
Graph RAG: Building Knowledge Graph-Enhanced Retrieval Pipelines for Complex Multi-Hop Queries
Standard RAG fails when answers require tracing relationships across multiple pieces of information. Graph RAG combines vector search with knowledge graph traversal to handle exactly these multi-hop queries—and this lesson shows you how to build the full pipeline from scratch, including ingestion, entity extraction, graph construction, and hybrid retrieval.
24 min read
AI & Machine Learning🔥 Expert
Agentic Loop Design: Building Reliable Multi-Step LLM Pipelines with Retry Logic, State Management, and Human-in-the-Loop Escalation
Most LLM agent tutorials show you the happy path. This lesson shows you the full picture — how to design agentic loops that survive tool failures, persist state across crashes, classify errors intelligently, and pause gracefully for human review without losing progress. By the end, you'll have the engineering foundations for agents you'd actually trust in production.
26 min read
AI & Machine Learning🔥 Expert
Retrieval-Augmented Generation in Practice: Building Knowledge-Grounded AI Pipelines for Enterprise Data Workflows
RAG sounds simple until you deploy it in production. This deep-dive lesson covers every layer of a real RAG pipeline — from chunking strategy and hybrid retrieval to cross-encoder reranking and RAGAS evaluation — with production-grade Python code you can actually use.
28 min read
AI & Machine Learning⚡ Practitioner
Tool-Augmented AI Agents: Giving Your Agent Web Search, Code Execution, and API Access
Language models alone can't access today's data, run precise calculations, or call external systems. Learn how to build production-ready tool-augmented agents with web search, sandboxed code execution, and REST API integration — including error handling, memory, and cost management.
21 min read
AI & Machine Learning⚡ Practitioner
Implementing Conversational Memory: Managing Context Windows and Chat History at Scale
Naive chatbots forget everything after a few dozen messages — or crash trying to hold it all in context. This lesson teaches you to build a production-grade tiered memory system that combines buffer management, summarization, and vector retrieval to give LLMs coherent, scalable long-term memory.
21 min read
AI & Machine Learning⚡ Practitioner
Structuring Unstructured Data with AI: Extracting Tables, Entities, and Insights from Text and Documents
Learn how to build production-grade AI extraction pipelines that turn messy contracts, invoices, press releases, and reports into clean, structured data. This hands-on lesson covers schema design, entity extraction, table reconstruction, long-document chunking, and confidence scoring — everything you need to move from demo to production.
24 min read