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From Prompt to Production... LLMs Done Right
California employers are hiring developers who can integrate AI into real products—securely, reliably, and at scale. This certification prepares you to design, build, evaluate, and deploy production-grade LLM-powered features.
We've designed the AI Application Engineering Certification to integrate with busy schedules and support your career advancement and skill growth with stackable, 1-unit courses focused on security, evaluation & cloud deployment built for working developers.
AI is no longer experimental. It's infrastructure.
Artificial intelligence is rapidly becoming part of everyday software, from chat assistants and document summarization tools to AI-powered search, automation, and decision support systems. At the center of this shift are Large Language Models (LLMs). And behind every AI feature is a team of engineers who design, build, secure, and deploy those systems responsibly.
While most AI programs focus on theory or prompt writing, this certification focuses on AI application engineering, the bridge between model capabilities and production software. You’ll move beyond prompt experiments, toy demos, and AI curiosity into:
- Retrieval-augmented generation (RAG)
- Tool/function calling and system orchestration
- Threat modeling for LLM systems
- Evaluation-driven guardrails
- Cloud deployment and monitoring
- Agents and multi-step workflows
- Production-grade API integration
Be on the Leading Edge of Innovation
Between 2020 and 2025, there were 63,000+ AI-aligned developer postings across California. Today we see approximately 9,500 new developer postings per month, with rapid growth in machine learning, APIs, scalability, and security.
Employers are seeking developers who can integrate LLM APIs into production systems, architect cloud-native AI applications, secure AI systems against prompt injection and data leakage, deploy and monitor AI features using modern DevOps practices, and build scalable, API-driven software.
This program directly targets those capabilities.
Is This Program For You?
The ideal candidate understands how to build applications and now wants to understand how to build AI-enabled applications that are secure, reliable, and production-ready. This includes:
- Software developers curious about AI integration
- Backend or full-stack engineers expanding their skills
- DevOps or cloud professionals working with AI workloads
- Computer science graduates expanding their skill sets
- Technical professionals who want practical AI experience
This is not a "no-code AI tools" course. It is designed for learners who already have foundational programming skills and want to understand how AI works inside real software systems.
Program Structure
The program consists of six courses offered in summer and fall 2026. Each course is short, focused, and designed for working professionals:
✔ 1 unit per course
✔ Complete each course in 3 weeks
✔ Fully online
✔ Hands-on labs and guided projects
✔ Courses stack into the full certification
✔ Fees: $450/course. Total cost for the certification: $2,700.*
Courses
Courses are taught by Neerja Bhatnagar, a graduate of the MS in Computer Science program from California State University, Chico, and a senior software engineer at Apple for nearly nine years.
Course 1: System Architecture & Request–Response Flows
Dates: June 8–26, 2026
Learn where LLMs fit as a component in a typical client-server architecture, request-response flows, and multiturn conversations. Learning outcomes:
· Client-server architecture with LLM as a component· LLM deployment· End-to-end request-response flow with LLMs· How to maintain message history in multiturn conversations
Course 2: System Prompt as a Code Parameter
Dates: July 6–24, 2026
Learn how to embed system prompts directly in your codebase as a reusable parameter, techniques to use images and .pdfs in prompts, and how to format and influence LLM responses. Learning outcomes:
· Learn to use system prompt as a reusable code parameter· Write better prompts using XML tags and examples· Use APIs to provide images & .pdfs as inputs in system prompt· Improve performance with prompt caching and TTL· Influence and control LLM responses with prefilled assistant messages and stop sequences· Upload and download files using files API
Course 3: Tool Use
Dates: Aug. 3–21, 2026
Learn how LLMs can utilize tools to access external data sources and how to convert a “normal” code function into tool use by the LLM. Learning outcomes:
· Learn the request-response flow for tool use· Learn step-by-step how to build a tool· How to handle errors when executing tools, stream tool use responses from LLM· A built-in tool example
Course 4: MCP (Model Context Protocol)
Dates: Aug. 31–Sept. 18, 2026
Learn about MCP client and server, and how to leverage tool use with MCP. Learning outcomes:
· Learn what MCP is· MCP architecture and the end-to-end request–response· MCP flow· How to build an MCP client and server
Course 5: Agents
Dates: Sept. 28–Oct. 16, 2026
Learn about agents, their types, features, and how they work. Learning outcomes:
· Learn the components that make up agent architecture· How to build an agent with tool use
Course 6: RAG (Retrieval-Augmented Generation)
Dates: Oct. 26–Nov. 13, 2026
Learn RAG concepts and workflows. Learning outcomes:
· Learn what RAG is
· Text chunking and text embedding
· RAG flow
· Example walkthrough
Applications Open April 1
To apply to the AI Application Engineering Certification program, you will be asked to upload a resumé and write a personal statement. There is no fee to apply, and applications will be reviewed on a rolling basis.
