DL.AIHands-on reviewed
DeepLearning.AI
Building and Evaluating Advanced RAG Applications
For: Engineers whose basic RAG works in dev but fails in prod
Advanced · ~1.5 hours (5 lessons) · Free
Building and Evaluating Advanced RAG ApplicationsvsVector Databases: from Embeddings to Applications
Side-by-side comparison on level, duration, pricing, instructor, tier. Editor verdict on which course wins for which buyer.
DL.AI
DeepLearning.AI
Building and Evaluating Advanced RAG Applications
The right course for the moment a 'just stuff it into the context window' RAG starts failing. Covers retrieval evaluation, sentence-window retrieval, auto-merging, and the production-failure modes that toy RAG demos don't surface. Free, taught by the LlamaIndex founder + the team behind TruLens evaluation. The pre-req: you should already have built a basic RAG and watched it answer questions wrong. If you haven't, start with a simpler intro.
DL.AI
DeepLearning.AI
Vector Databases: from Embeddings to Applications
If you're going to use a vector DB (and in 2026 most AI engineers will), this is the right 90 minutes to spend. Covers embeddings, ANN algorithms, sparse vs dense, hybrid search, and a head-to-head of Pinecone, Weaviate, Chroma and pgvector. Free, vendor-agnostic enough despite the Weaviate teaching credit. Take before you commit to a vector DB.
Side-by-side
| Dimension | Building and Evaluating Advanced RAG Applications | Vector Databases: from Embeddings to Applications |
|---|---|---|
| Provider | DeepLearning.AI | DeepLearning.AI |
| Editorial tier | Hands-on reviewed | Hands-on reviewed |
| Level | Advanced | Intermediate |
| Format | self paced | self paced |
| Duration | ~1.5 hours (5 lessons) | ~1.5 hours (5 lessons) |
| Pricing | Free | Free |
| Instructor | Jerry Liu & Anupam Datta — Founder, LlamaIndex; Co-Founder, TruEra | Sebastian Witalec — Head of Developer Relations, Weaviate |
| Rating | No public rating | No public rating |
| Topics | rag systems, ai evals | rag systems, llm fundamentals |
| Last verified | 2026-05-23 | 2026-05-24 |
Pros & cons
Building and Evaluating Advanced RAG Applications
Pros
- +Production-grade content — failure modes, not just happy paths
- +Free, 90 minutes, by LlamaIndex + TruEra principals
- +Evaluation is the under-covered topic in the RAG space
Cons
- −Assumes you already understand basic retrieval
- −LlamaIndex-flavored — if you're a LangChain shop, mental translation needed
Vector Databases: from Embeddings to Applications
Pros
- +Vendor-agnostic — covers Pinecone, Weaviate, Chroma, pgvector
- +First-principles approach — you understand WHY, not just HOW
- +Free, 90 minutes
Cons
- −Light on production-ops (sharding, backups, hybrid filtering)
Which course is for whom?
Building and Evaluating Advanced RAG Applications
Best for
- · Engineers whose basic RAG works in dev but fails in prod
- · AI engineers debugging hallucinations on retrieved context
Not ideal for
- · People building their first RAG — start simpler
- · Anyone allergic to LlamaIndex idioms
Vector Databases: from Embeddings to Applications
Best for
- · Engineers about to commit to a vector DB choice
- · PMs scoping a RAG project who need to understand the storage layer
Not ideal for
- · People wanting a single-vendor deep-dive — see Pinecone Learn or Weaviate Academy
Editor's short verdict
Take Vector Databases: from Embeddings to Applications first if you're new to the topic; once you have the basics, Building and Evaluating Advanced RAG Applications is the natural next step. They're complementary in a learning path, not directly competing.
DL.AIHands-on reviewed
DeepLearning.AI
Vector Databases: from Embeddings to Applications
For: Engineers about to commit to a vector DB choice
Intermediate · ~1.5 hours (5 lessons) · Free
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