Interview track
LLMs, RAG, and inference — learn the system, not the slogan
From why a model invents a leave policy to vector search, evaluation, agents, and the serving stack that keeps the API fast.
Eleven modules from the limits of a raw LLM to production RAG, inference, and the evaluation metrics panels ask you to define. One HR assistant, PolicyDesk, carries every example.
- 31 lesson checkpoints
- Linked AI practice bank
11 modules31 lessons7 hr 26 min reading~5 weeks at 30 min/dayCurriculum reviewed Oct 2026
FreeAll 5 L1 lessons in full.
ProThe other 26 lessons (L2–L4). Each opens with a free preview.
Compare plansStart here
Limitations of LLMs and Why RAG Exists
AI interview preparation guide
Eleven modules from the limits of a raw LLM to production RAG, inference, and the evaluation metrics panels ask you to define. One HR assistant, PolicyDesk, carries every example.
Open the interactive AI path · 11 sections · 31 lessons
Syllabus overview
- S01 — Why LLMs Need RAGCutoffs, hallucinations, private data, and what retrieval changes
- S02 — RAG ArchitectureThe six-step pipeline from a user question to a grounded answer
- S03 — Ingestion and ChunkingLoaders, cleaning, chunk strategies, and metadata
- S04 — Embeddings and Vector SearchMeaning as vectors, similarity metrics, and vector databases
- S05 — Retrieval and PromptsSimilarity, keywords, hybrid search, and prompts that use context
- S06 — Build and ConnectAn end-to-end pipeline, many data sources, and four starter projects
- S07 — Advanced RAGBetter retrieval, evaluation metrics, architectures, and agents
- S08 — Production and the EcosystemDeploy, operate, fix the common failures, and pick tools
- S09 — Interview RevisionThe questions panels actually ask, with answers you can say out loud
- S10 — LLM Inference and ServingA microservice, optimizations, KV cache, batching, and autoscaling
- S11 — Quality, Documents, and SafetyHallucinations, judging, versions, tables, metrics, and injection