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 LlamaIndexvsPinecone Learn (vector DB + RAG)
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.
PC
Pinecone Learn
Pinecone Learn (vector DB + RAG)
Pinecone's free learn portal. Despite being vendor-published, the content is genuinely vendor-agnostic for the first 60% (embedding theory, ANN basics, hybrid search) and only becomes Pinecone-specific in deployment chapters. James Briggs is one of the best practical-RAG explainers in the field. Free, continuously updated, with code examples that run.
Side-by-side
| Dimension | Building Agentic RAG with LlamaIndex | Pinecone Learn (vector DB + RAG) |
|---|---|---|
| Provider | DeepLearning.AI | Pinecone Learn |
| Editorial tier | Hands-on reviewed | Curated |
| Level | Advanced | Intermediate |
| Format | self paced | self paced |
| Duration | ~2 hours (4 lessons) | Variable (~15-25 hours full series) |
| Pricing | Free | Free |
| Instructor | Jerry Liu — Co-founder, LlamaIndex | Pinecone DevRel + James Briggs — Pinecone team + community |
| 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
Pinecone Learn (vector DB + RAG)
Pros
- +James Briggs is one of the clearest RAG writers in the field
- +First 60% of content is vendor-agnostic
- +Free, regularly updated
Cons
- −Deployment chapters are Pinecone-specific
- −No structured progression — choose-your-own-adventure format
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
Pinecone Learn (vector DB + RAG)
Best for
- · Engineers learning vector DBs and RAG from first principles
- · Anyone evaluating Pinecone vs alternatives
Not ideal for
- · Learners who need a guided curriculum — pick HF or DL.AI courses instead
Editor's short verdict
Take Pinecone Learn (vector DB + RAG) 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.
PCCurated
Pinecone Learn
Pinecone Learn (vector DB + RAG)
For: Engineers learning vector DBs and RAG from first principles
Intermediate · Variable (~15-25 hours full series) · Free
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