Generative AI Application Developer 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 “Generative AI Application Developer Bootcamp Certificate” instead.
Bootcamp Mastery Certificate B08
Primary spine: Microsoft Developer - Generative AI for Beginners, reinforced with the 2025 AI Dev Days production bridge. Build beyond prompting: text/chat, vector search, multimodal apps, function calling, secure UX, RAG, open models, agents, fine-tuning, MCP and observability.
Benchmark: Microsoft publishes Generative AI for Beginners as a free learning series, and its AI Dev Days sessions are public video resources. The Zuyini Bootcamp Mastery Certificate is an independent curated build-and-proof credential and does not represent a Microsoft-issued certification.
Core Video Sequence
01 — Complete generative AI application series
02 — Model choice, responsible AI + prompting
03 — Text generation + chat application patterns
04 — Vector search + retrieval application design
05 — Multimodal + image generation applications
06 — Function calling + external tool integration
07 — AI UX, security + trust boundaries
08 — Application lifecycle + RAG architecture
09 — Open models, agents + fine-tuning
10 — Current bridge: multi-agent, MCP + observability
Build-Along Checkpoints
- Checkpoint 1: Build a text/chat baseline with explicit system instructions, structured outputs, token/cost tracking and failure examples.
- Checkpoint 2: Add a vector-search or RAG path with source attribution; measure retrieval quality separately from answer quality.
- Checkpoint 3: Add function calling or a tool integration with schema validation, authorization boundaries and graceful failure handling.
- Checkpoint 4: Add one multimodal capability or open-source model path and document model-choice quality, latency, cost and privacy tradeoffs.
- Checkpoint 5: Create an eval set with task-success, groundedness, safety and latency/cost criteria; run regression checks across versions.
- Checkpoint 6: Deploy with traces, usage/quality telemetry and one agent/MCP workflow; simulate a tool/model failure and show recovery.
7-Day Independent Capstone
- Day 1: Choose a real user workflow and acceptance criteria; map data, model, tools, trust boundaries, risks and fallback behavior.
- Day 2: Build the core text/chat experience with versioned prompts, structured outputs and reproducible test cases.
- Day 3: Add retrieval or vector search and create a small gold set that separates retrieval failures from generation failures.
- Day 4: Add at least one external function/tool plus a multimodal or open-model capability; secure and validate every boundary.
- Day 5: Build automated evals for task success, groundedness, safety, latency and cost; compare two model/configuration choices.
- Day 6: Deploy with traces/telemetry, an agent or MCP integration, rate/error handling and one injected production failure.
- Day 7: Publish repo, architecture, eval report, threat/trust-boundary note, cost/latency dashboard, demo video and runbook.
Generative AI Apps - Build, Evaluate & Ship
Ship a useful generative AI application rather than a prompt demo. The final system must combine model selection, structured prompting, retrieval or tools, safe UX, explicit evaluation, telemetry and a production deployment path; advanced learners should add an agent or MCP integration.
Proof Rubric — What Must Be Shown
- UX: Useful workflow + clear fallback behavior
- PROMPTS: Versioned instructions + structured outputs
- GROUNDING: Retrieval quality + source attribution
- TOOLS: Validated calls + least-authority boundaries
- EVALS: Task success, safety + regression evidence
- ECONOMICS: Latency, token use + model tradeoffs
- OBSERVABILITY: Traces, telemetry + incident evidence
- AGENT/MCP: Working integration + bounded autonomy