DevelopmentIntermediate42 lessons6 modules

RAG & Knowledge Systems

Build retrieval-augmented generation systems that give AI accurate, up-to-date knowledge. Vector databases, embedding strategies, chunking, and evaluation.

$49per month — this curriculum and the other 13

Courses are not sold one at a time. One subscription opens this curriculum and every other one, each with its own coach. See the full terms. Card required to start the trial; the first payment lands on day 8 and renews until you cancel.

What's Included

The full curriculum — 6 modules, 42 lessons
An AI coach that knows this curriculum and your business
Progress tracking across the curriculum

No videos, no written lesson pages, no quizzes, no certificate. You work through the curriculum in conversation with the coach.

7-Day Money-Back Guarantee

Not satisfied? Get a full refund within 7 days. No questions asked.

What You'll Learn

Understand embeddings: how text becomes vectors and why it matters

Set up and query vector databases (Pinecone, Weaviate, Chroma)

Implement chunking strategies optimized for different document types

Build retrieval pipelines with hybrid search and reranking

Combine keyword search with semantic search for better results

Evaluate RAG quality with automated metrics and human review

Deploy production RAG pipelines with caching and monitoring

Integrate knowledge graphs with vector search for richer context

Outcomes

Build production RAG systems with vector databases and embedding pipelines
Implement advanced chunking and retrieval strategies for accuracy
Evaluate and optimize RAG performance with systematic frameworks
Deploy knowledge systems that handle enterprise-scale document collections
Complete 3 milestone projects: document Q&A, multi-source knowledge base, and production RAG pipeline

Prerequisites

-Python fundamentals
-Basic understanding of APIs
-Familiarity with databases helpful

Projects You'll Build

Build a document Q&A system with Pinecone/Weaviate
Create a multi-source knowledge base with hybrid search
Deploy a production RAG pipeline with evaluation metrics

Course Curriculum

Module 1: RAG Quick Start & Embeddings

1.1Quick Start: Build a working RAG system in 30 minutes
1.2What are embeddings and why they power modern AI search
1.3Embedding models compared: OpenAI, Cohere, Voyage, open-source
1.4Vector similarity: cosine, dot product, and Euclidean distance
1.5Dimensionality and its effect on retrieval quality
1.6Generating embeddings via API and locally with sentence-transformers
1.7Visualizing embeddings to understand your data

Module 2: Vector Databases

2.1Why you need a vector database (not just numpy)
2.2Pinecone: setup, indexing, querying, and namespaces
2.3Weaviate: schema design, hybrid search, and modules
2.4Chroma: the lightweight local-first option
2.5PostgreSQL pgvector: adding vectors to your existing database
2.6Choosing the right vector database for your use case
2.7Index types and performance tuning (HNSW, IVF, PQ)

Module 3: Chunking & Ingestion

3.1Why chunking strategy makes or breaks RAG quality
3.2Fixed-size chunking with overlap
3.3Semantic chunking: splitting by meaning, not characters
3.4Document-aware chunking: headers, paragraphs, code blocks
3.5Recursive chunking for hierarchical documents
3.6Metadata extraction: titles, dates, authors, sections
3.7Building ingestion pipelines for PDF, HTML, Markdown, and DOCX

Module 4: Retrieval Strategies

4.1Basic semantic search and its limitations
4.2Hybrid search: combining BM25 keyword search with vector similarity
4.3Reranking: using cross-encoders to improve result quality
4.4Multi-query retrieval: generating query variations for better recall
4.5Contextual compression: extracting relevant passages from chunks
4.6Parent document retrieval: small chunks for search, large chunks for context

Module 5: Evaluation & Optimization

5.1Why RAG evaluation is essential (garbage in, garbage out)
5.2Retrieval metrics: precision, recall, MRR, and NDCG
5.3Generation metrics: faithfulness, relevance, and completeness
5.4Building evaluation datasets from real user queries
5.5Automated evaluation with RAGAS and custom frameworks
5.6A/B testing retrieval strategies in production
5.7Common failure modes and how to diagnose them

Module 6: Production RAG Systems (Capstone)

6.1End-to-end RAG architecture for production
6.2Caching strategies: query cache, embedding cache, result cache
6.3Incremental indexing: updating knowledge without full reindexing
6.4Multi-tenant RAG: isolating knowledge per user or organization
6.5Knowledge graphs: adding structured relationships to vector search
6.6Monitoring retrieval quality in production with feedback loops
6.7Scaling RAG: handling millions of documents and concurrent queries
6.8Capstone Exercise: Deploy your RAG pipeline end-to-end and benchmark its performance

Related Courses

Build AI Apps with Claude API

59 lessons · $147

Prompt Engineering Mastery