Teacher since August 2025
Emeka Nwosu
Search engineer teaching embeddings, chunking, vector and keyword search, and reranking from first principles
9
tutors built
4.6Sample
average from 18 reviews
374Sample
lessons taught by their tutors
About Emeka
I come from search: indexes, ranking and the long tail of queries that make a search box look foolish. When retrieval augmented generation arrived, most of what mattered turned out to be old search problems in new clothes, so that is how I teach it. We start with how text becomes something you can compare, then how documents are split, stored, searched and reranked, and we look at real results side by side to see why one approach finds the answer and another does not. I explain vector databases without the marketing and I always keep keyword search on the table.
Knows about
Tutors by Emeka
9 tutors
RAG Explained for Non Engineers
Understand how AI assistants answer from company documents, what can go wrong and what to ask your team.66 lessonsSampleEmeka NwosuFreeVector Databases for BeginnersVector Databases for Beginners
Learn what a vector database does, when you need one and how to choose without falling for marketing.63 lessonsSampleEmeka Nwosu$4Your First RAG Pipeline, End to EndYour First RAG Pipeline, End to End
Build a small retrieval augmented chatbot over your own documents, step by step, and see where it fails.59 lessonsSampleEmeka NwosuFreeMetadata Filters and Permission Aware RetrievalMetadata Filters and Permission Aware Retrieval
Filter retrieval by date, product or tenant and make sure users only ever retrieve what they may see.57 lessonsSampleEmeka Nwosu$11Embeddings Explained for BuildersEmbeddings Explained for Builders
Understand what embeddings capture, what they miss and how to use them for search, grouping and matching.53 lessonsSampleEmeka Nwosu$4Chunking Documents for RetrievalChunking Documents for Retrieval
Split documents into retrievable chunks that keep their meaning, then tune chunk size with real measurements.41 lessonsSampleEmeka Nwosu$6Reranking Retrieved ResultsReranking Retrieved Results
Add a reranking stage that puts the truly relevant passages first, within your latency and cost budget.35 lessonsSampleEmeka Nwosu$10Keyword Search and Inverted IndexesKeyword Search and Inverted Indexes
Understand how keyword search really works, from inverted indexes to BM25, and tune it for your content.Emeka Nwosu$6Hybrid Search: Keywords Plus VectorsHybrid Search: Keywords Plus Vectors
Combine keyword and vector search so you find both exact codes and loosely worded questions.Emeka Nwosu$8Recent reviews
What students said about Emeka's tutors.
- Svetlana I.Sample
We measured first stage recall and found the reranker was not the problem at all. Fixing chunking first and then reranking gave a clear jump in nDCG.
- Andre B.Sample
The explanation of why cross encoders catch negation was excellent. Threshold calibration section was short but practical.
- Vikram J.Sample
Looking at real chunks and asking whether a person could answer from them was the best habit I picked up. Table handling section was brief but useful.
- Margaret O.Sample
As a knowledge manager I finally understand why our content quality matters so much for the AI project. The vendor questions list was gold.
- Karin V.Sample
Our shared index relied on developers remembering the tenant filter. We moved it into one central function and added the permission test suite from lesson six.
- Ricardo P.Sample
Free, clear and no jargon. The point that RAG cannot count across all documents saved us from a bad use case.