RAG Fundamentals: Build Production AI Systems | 57 Lessons | EducationPals.ai
technical · RAG & Knowledge Systems
Master RAG: Build AI That Actually Knows WhatIt's Talking About
Learn Retrieval-Augmented Generation from the ground up — 13 chapters, 57 lessons, and the skills to build AI systems grounded in real, accurate knowledge.
From $9/month·~10 hrs·5 chapters
5chapters
21lessons
13frameworks
“🚨 Your LLM is hallucinating. RAG is the fix — and in 57 lessons, you'll know exactly how to build it. Here's what most AI devs are still missing...”
Curriculum
5 chapters, 21 lessons
The full expedition — every chapter and lesson. Tap a chapter to expand. Lessons unlock when you start.
⊘The Amnesia Problem: Why LLMs Fabricate When They Don't Know
⊘RAG in 60 Seconds: The Retrieve-Then-Generate Loop
⊘Four Paths, One Problem: RAG vs. Fine-Tuning vs. Prompting vs. Long Context
⊘Opening Almanac's First Case File: The Sable & Whitmore Challenge
⊘Two Pipelines, One System: Ingestion and Query Are Not the Same Thing
⊘The Seven Stations: What Each RAG Component Actually Does
⊘No Neutral Components: How Data Quality Degrades Across the Pipeline
⊘Choosing Your Stack: Frameworks and Tools Without the Hype
⊘Source Diversity: Why PDFs, Web Pages, and Databases Each Break Differently
⊘Metadata Matters: What Gets Lost When You Skip the Tags at Intake
⊘The Cleaning Room: Preprocessing Steps That Actually Change Retrieval Outcomes
⊘Building Almanac's First Loader: Ingesting the Sable & Whitmore Corpus
⊘Why Chunks: The Goldilocks Problem Between Too Much and Too Little Context
⊘Four Strategies, Four Trade-Offs: Fixed, Recursive, Semantic, and Structural Chunking
⊘The Overlap Myth: Why More Overlap Doesn't Always Mean Better Retrieval
⊘When Good Documents Make Bad Chunks: Structure Violations and Severed Citations
⊘Almanac's Chunking Layer: Why Legal Documents Demand a Custom Strategy
⊘From Words to Coordinates: How Embeddings Encode Semantic Meaning
⊘Model Selection Is Not Neutral: Size, Speed, Domain Fit, and What Gets Sacrificed
⊘Cosine Similarity Is Not a Truth Score: Distance Metrics and Their Limits
⊘When Fingerprints Lie: Negation, Domain Shift, and Semantic Collapse
Why it's worth it
The credential that closes the gap
These frameworks map to roles hiring teams actively recruit for. Browse open jobs to see the market.
A few of the misconceptions this course clears up. The full set is inside.
“If you connect an LLM to your documents, it will automatically stop hallucinating.”
RealityRAG reduces hallucination by grounding responses in retrieved evidence, but it does not eliminate fabrication. An LLM can still confabulate when retrieved chunks are irrelevant, incomplete, or contradictory — it will simply hallucinate with more confidence, sometimes even citing the wrong passage as justification. The SUMMONS Protocol exists precisely because retrieval must be treated as a structured demand for evidence, not a magic anti-hallucination switch.
“RAG is just one pipeline — you build it once and data flows straight through.”
RealityA production RAG system contains two fundamentally separate pipelines that must never be confused: the ingestion pipeline (which runs offline to process and index documents) and the query pipeline (which runs in real time to retrieve and generate answers). Contaminating these two — for example, re-chunking documents on every query, or applying query transformations during ingestion — is one of the most common architectural mistakes Perigon's team has seen destroy latency and consistency in client deployments.
“You can ingest any document format directly into your RAG system without preprocessing.”
RealityRaw documents — PDFs, Word files, HTML pages, spreadsheets — are full of structural noise: headers, footers, watermarks, encoding artifacts, inconsistent whitespace, and embedded tables that shatter into gibberish when extracted naively. The PROCURE Pipeline requires every document to be Probed, Routed, Opened, Cleaned, and Unified before it ever reaches the index. Skipping these steps means your vector store is fingerprinting garbage, and your retrieval will surface garbage in return.
Frameworks you'll keep
Portable thinking tools
Named frameworks you'll carry into every AI decision long after the course.
RAG is a technique that augments large language models with real-time access to external knowledge sources, allowing them to retrieve relevant documents before generating responses. This solves two critical problems: hallucinations (false information) and knowledge cutoff (outdated information). RAG is now the dominant pattern for building reliable, factual AI systems in enterprise applications.
Fine-tuning bakes knowledge into model weights through expensive retraining, making updates slow and costly. RAG keeps model weights frozen and dynamically retrieves current information at query time, making it far cheaper and more flexible. For applications where information changes frequently—like company docs, legal databases, or news—RAG is the clear winner.
You'll benefit from basic Python skills and a conceptual understanding of how LLMs work, but deep ML expertise isn't required. The course builds RAG concepts from the ground up, making it accessible to software engineers, data engineers, and AI practitioners who want to move beyond theory into building real systems.
You'll work with industry-standard tools including vector databases (Pinecone, Weaviate, ChromaDB), embedding models (OpenAI, open-source alternatives), and orchestration frameworks (LangChain, LlamaIndex). The course teaches you how these components fit together in a real RAG stack, preparing you for the tools used in production environments.
A RAG system has two main pipelines: the ingestion pipeline (loaders, chunkers, embedders, vector stores) that prepares knowledge, and the query pipeline (retriever, reranker, prompt engine, generator) that answers questions. The course covers each component in depth, including architecture patterns, trade-offs, and implementation strategies.
RAG systems require dual evaluation: retrieval metrics (precision, recall, NDCG) measure whether the right documents are found, while generation metrics (faithfulness, relevance, groundedness) measure answer quality. The course teaches you how to build evaluation datasets and choose between automated and human evaluation approaches.
After this course, you can build document Q&A systems, enterprise chatbots, AI customer support tools, research assistants, and any application requiring an LLM to reason over private or frequently updated data. These are among the most commercially valuable AI applications being deployed today.
The course teaches a systematic debugging framework covering retrieval failures (missing, irrelevant, or redundant results) and generation failures (hallucinations, refusals, incoherence). You'll learn to isolate whether problems stem from retrieval, ranking, or prompt engineering—critical skills for production systems.
Beyond fundamentals, you'll explore hybrid search (dense + sparse fusion), cross-encoder reranking, recursive retrieval, parent-child chunk strategies, and query transformation techniques like HyDE. These advanced methods significantly improve retrieval quality in complex, real-world scenarios.
The course covers production deployment patterns, scaling strategies for ingestion and query pipelines, monitoring and observability (latency, retrieval quality, cost), and user feedback loops for continuous improvement. You'll understand the operational considerations that separate prototype RAG systems from production-grade ones.
Yes. This course assumes you're comfortable with Python and have some experience with APIs or libraries. You don't need to be an expert, but you should be able to read and understand code. The course focuses on architecture and decision-making, not syntax—but you'll be implementing concepts, not just learning theory.
Papers and blog posts teach you isolated concepts. This course teaches you how those concepts fit together into a coherent system. You'll understand not just what each component does, but why it matters, when to use it, and what trade-offs you're making. It's the difference between knowing about RAG and being able to architect a RAG system.
No. This course teaches you the underlying principles and architecture of RAG systems. Those principles apply regardless of which framework you use. By understanding the fundamentals, you'll be able to evaluate and use any RAG tool effectively—and you'll know when to build custom solutions instead.
The course is about 10 hours of learning — roughly 2 weeks at ~5 hours per week. All materials are available on-demand, so you can move faster or slower depending on your schedule.
You can skip ahead to the chapters that are new to you. The course is structured so you can jump in at any point. That said, even experienced practitioners often find value in the chapters on evidence fingerprinting, reranking logic, and response generation—these are where most production failures happen.
Yes. You'll receive a certificate of completion that you can share on LinkedIn or include in your professional profile. More importantly, you'll have the skills and mental model that the certificate represents—the ability to architect and explain RAG systems end to end.
EducationPals includes community access for all learners. You can ask questions in the course forum, and you'll get responses from instructors and other learners. The course also includes office hours where you can ask questions directly.
Yes. The fundamentals of RAG are stable, but the course is updated quarterly to reflect new techniques, tools, and best practices. Once you enroll, you have lifetime access to all updates—no additional cost.