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RAG, Fine-tuning, and Evaluation
Retrieval-augmented generation done properly; embeddings and vector databases; when fine-tuning is worth it (and when it isn't); evaluating LLM outputs in ways that actually catch bugs.
Module Content
Retrieval-Augmented Generation (RAG) Foundations
The most-used LLM pattern in production. Give the LLM your data at query time. FIND_VIDEO: search 'RAG retrieval augmented generation tutorial' — recommended channel: James Briggs / Greg Kamradt / DeepLearning.AI. Aim for 11 min or under.
Recap — Building a Working RAG Pipeline
RAG sounds simple. Production RAG is a pipeline of 5+ pieces, each of which has subtleties. Here's the foundation.
Embeddings and Vector Databases
The pieces under RAG. What embeddings actually represent and how vector DBs serve them at scale. FIND_VIDEO: search 'embeddings vector database tutorial pinecone qdrant' — recommended channel: James Briggs / Pinecone. Aim for 10 min or under.
Recap — Choosing an Embedding Model and Vector Store
Embedding choices and vector store choices interact. Pick by your scale, domain, and budget.
When to Fine-tune (and When Not To)
The fine-tuning decision matrix. Most teams reach for it too soon. FIND_VIDEO: search 'LLM fine tuning when to fine tune LoRA' — recommended channel: DeepLearning.AI / Anthropic / Trelis Research. Aim for 10 min or under.
Recap — Fine-tuning Patterns and Costs
Fine-tuning fundamentals: when it pays off, what data you need, and the LoRA/QLoRA patterns that make it cheap.
Evaluating LLM Outputs — The Hard Problem
LLMs produce open-ended outputs. Evaluating them requires more than accuracy. Here's what actually works. FIND_VIDEO: search 'LLM evaluation metrics RAG eval' — recommended channel: DeepLearning.AI / Greg Kamradt / AI Engineer. Aim for 10 min or under.
Recap — LLM Eval That Actually Catches Bugs
Three layers of LLM evaluation. The teams that don't do this ship invisible regressions; the ones that do iterate confidently.