RAG Pipeline

Construct ingestion, chunking, embeddings, hybrid retrieval, grounded generation, evaluation, and conversational memory.

8 parts51 individual lessons4.3 estimated hours255 XP available

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Every source step is its own lesson with intuition, concepts, correctly rendered MathJax mathematics, implementation, tests, mistakes, and a checkpoint.

PART 1

Document Ingestion & Preprocessing

Load text from files, HTML, and directories, normalize it, and wrap it into document objects with metadata.

0/5

001define document ingestion preprocessing+5 XP 002derive document ingestion preprocessing+5 XP 003prepare document ingestion preprocessing+5 XP 004implement document ingestion preprocessing+5 XP 005connect document ingestion preprocessing+5 XP

PART 2

Chunking Strategies

Split documents into retrievable units using fixed-size, token-based, sentence-aware, and overlapping chunkers with proper metadata.

0/5

006define chunking strategies+5 XP 007derive chunking strategies+5 XP 008prepare chunking strategies+5 XP 009implement chunking strategies+5 XP 010connect chunking strategies+5 XP

PART 3

Embeddings & Corpus Storage

Load a sentence-transformer model, embed and normalize chunks, and persist the corpus to disk.

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011define embeddings corpus storage+5 XP 012derive embeddings corpus storage+5 XP 013prepare embeddings corpus storage+5 XP 014implement embeddings corpus storage+5 XP 015connect embeddings corpus storage+5 XP

PART 4

Dense Retrieval with NumPy and FAISS

Implement cosine similarity search from scratch, then accelerate it with a FAISS index and verify the two agree.

0/8

016define dense retrieval with numpy and faiss+5 XP 017derive dense retrieval with numpy and faiss+5 XP 018prepare dense retrieval with numpy and faiss+5 XP 019implement dense retrieval with numpy and faiss+5 XP 020connect dense retrieval with numpy and faiss+5 XP 021validate dense retrieval with numpy and faiss+5 XP 022debug dense retrieval with numpy and faiss+5 XP 023optimize dense retrieval with numpy and faiss+5 XP

PART 5

Prompting and Answer Generation

Assemble grounded prompts, load a local instruct model, generate answers, and attach source citations to outputs.

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024define prompting and answer generation+5 XP 025derive prompting and answer generation+5 XP 026prepare prompting and answer generation+5 XP 027implement prompting and answer generation+5 XP 028connect prompting and answer generation+5 XP 029validate prompting and answer generation+5 XP 030debug prompting and answer generation+5 XP 031optimize prompting and answer generation+5 XP 032benchmark prompting and answer generation+5 XP

PART 6

Advanced Retrieval Techniques

Improve recall and precision with query rewriting, HyDE, BM25, hybrid search, cross-encoder reranking, MMR, and metadata filtering.

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033define advanced retrieval techniques+5 XP 034derive advanced retrieval techniques+5 XP 035prepare advanced retrieval techniques+5 XP 036implement advanced retrieval techniques+5 XP 037connect advanced retrieval techniques+5 XP 038validate advanced retrieval techniques+5 XP 039debug advanced retrieval techniques+5 XP 040optimize advanced retrieval techniques+5 XP

PART 7

Evaluation

Build an eval set and implement retrieval and answer-quality metrics like hit rate, recall@k, MRR, faithfulness, and relevance.

0/6

041define evaluation+5 XP 042derive evaluation+5 XP 043prepare evaluation+5 XP 044implement evaluation+5 XP 045connect evaluation+5 XP 046validate evaluation+5 XP

PART 8

Robustness, Caching, and Chat Memory

Add abstention, deduplication, embedding caching, and conversational memory with follow-up rewriting.

0/5

047define robustness caching and chat memory+5 XP 048derive robustness caching and chat memory+5 XP 049prepare robustness caching and chat memory+5 XP 050implement robustness caching and chat memory+5 XP 051connect robustness caching and chat memory+5 XP