DL.AIHands-on reviewed
DeepLearning.AI
Building Agentic RAG with LlamaIndex
For: Engineers whose basic RAG works in dev but fails in prod
Advanced · ~2 hours (4 lessons) · Free
Building Agentic RAG with LlamaIndexvsVector 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 Agentic RAG with LlamaIndex
The follow-up to Building and Evaluating Advanced RAG. Where the earlier course covered retrieval-engineering fundamentals, this one introduces agentic RAG — the loop where the agent decides what to retrieve, evaluates the result, and re-queries if needed. Production-grade pattern that solves the 'hallucination on retrieved context' problem most basic RAG apps have. Free, taught by Jerry Liu, the LlamaIndex co-founder.
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 Agentic RAG with LlamaIndex | 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 | ~2 hours (4 lessons) | ~1.5 hours (5 lessons) |
| Pricing | Free | Free |
| Instructor | Jerry Liu — Co-founder, LlamaIndex | Sebastian Witalec — Head of Developer Relations, Weaviate |
| Rating | No public rating | No public rating |
| Topics | rag systems, build ai agents, ai evals | rag systems, llm fundamentals |
| Last verified | 2026-05-24 | 2026-05-24 |
Pros & cons
Building Agentic RAG with LlamaIndex
Pros
- +Solves a real production-RAG failure mode (single-shot retrieval misses context)
- +Free, by LlamaIndex co-founder
- +Material follow-up to the basic-RAG course
Cons
- −Strong pre-reqs: basic RAG + LlamaIndex familiarity assumed
- −LlamaIndex-flavored — patterns transfer but examples are vendor-specific
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 Agentic RAG with LlamaIndex
Best for
- · Engineers whose basic RAG works in dev but fails in prod
- · Anyone building research agents (Perplexity-style)
Not ideal for
- · First-time RAG builders — take Building and Evaluating Advanced RAG first
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 Agentic RAG with LlamaIndex 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
Other comparisons
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- Compare Building Agentic RAG with LlamaIndex vs Building and Evaluating Advanced RAG Applications →
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- Compare Vector Databases: from Embeddings to Applications vs Weaviate Academy (open-source RAG) →
- Compare Vector Databases: from Embeddings to Applications vs Pinecone Learn (vector DB + RAG) →
- Compare Vector Databases: from Embeddings to Applications vs Building and Evaluating Advanced RAG Applications →