LLM & RAG Engineering Bootcamp Certificate
Open a video or resource link, watch it, then check its box to track your progress and unlock what comes next.
Search Live Openings
We don’t have a direct job-board feed matched to this one yet, so here are real, live US job searches for “LLM & RAG Engineering Bootcamp Certificate” instead.
Bootcamp Mastery Certificate B04
Build production-style LLM applications rather than train foundation models: agentic RAG, vector search, orchestration, systematic evaluation, monitoring and a complete knowledge-grounded application. The 2026 video sequence is the current implementation spine.
Benchmark: DataTalksClub LLM Zoomcamp -- DataTalksClub LLM Zoomcamp is a free video-led course and uses project-based certificate rules during eligible cohorts; provider certificate availability depends on the active cohort, deadlines and peer-review requirements. This is a Zuyini Bootcamp Mastery Certificate built around free LLM Zoomcamp video materials, using the independent build-and-proof rubric below.
Core Video Sequence
01 — Current canonical lecture sequence
02 — 2026 cohort recordings & current implementation
03 — Environment, uv, dependencies & API setup
04 — RAG architecture: retrieval + context + generation
05 — Knowledge-base ingestion & FAQ dataset
06 — Keyword retrieval, filtering & boosting
07 — Context construction & prompt assembly
08 — Reusable RAG pipeline / application structure
09 — Foundational LLM + RAG walkthrough
10 — Vector search, orchestration, evals & monitoring
Build-Along Checkpoints
- Checkpoint 1: Build a RAG pipeline from a real knowledge base: ingest, index, retrieve, construct context and call an LLM.
- Checkpoint 2: Implement vector search with embeddings and compare it with keyword retrieval using measurable retrieval metrics.
- Checkpoint 3: Add an orchestration or agentic workflow where model/tool decisions are explicit and inspectable.
- Checkpoint 4: Create ground-truth questions and run offline retrieval + answer evaluation, including LLM-as-a-judge where appropriate.
- Checkpoint 5: Capture latency, token/cost and quality/feedback signals; persist conversations and expose monitoring dashboards.
- Checkpoint 6: Package a user-facing app and run the full system with a reproducible local/containerized environment.
7-Day Independent Capstone
- Day 1: Choose a domain and knowledge base; define users, failure risks, answer policy, target metrics and architecture.
- Day 2: Build ingestion + retrieval; compare keyword and vector search; capture a reproducible evaluation dataset.
- Day 3: Build the RAG application with prompt/context assembly, citations/grounding and one agent/tool workflow.
- Day 4: Evaluate retrieval and answers; tune search/chunking/prompts using metrics rather than anecdotal examples.
- Day 5: Add UI/API plus persistence, feedback capture, latency/token/cost logging and a quality-monitoring view.
- Day 6: Containerize/run end to end; simulate one bad retrieval, hallucination or dependency failure and document mitigation.
- Day 7: Publish repository, architecture, evaluation report, demo video, runbook and a short production-readiness memo.
Grounded LLM Application Capstone
Choose a domain and knowledge base, build ingestion and retrieval comparing keyword and vector search with a reproducible evaluation dataset, build a RAG application with prompt/context assembly, citations and one agent/tool workflow, run offline retrieval and answer evaluation, add UI/API with persistence and latency/token/cost monitoring, simulate and mitigate one failure mode, and publish a repository, architecture, evaluation report, demo video, runbook and production-readiness memo.
Proof Rubric — What Must Be Shown
- GROUNDING: knowledge base + traceable context
- RETRIEVAL: keyword/vector search + metrics
- AGENTIC FLOW: tool/workflow decisions are inspectable
- EVALUATION: ground truth + retrieval/answer evals
- OBSERVABILITY: latency, token/cost, quality signals
- APP: usable interface/API + persistence
- RELIABILITY: failure test + mitigation evidence
- COMMUNICATION: repo, report, runbook + demo video