Chico State Professional & Continuing Education

Program Highlights | Career Outlook | Courses | Apply Today!

From Prompt to Production... LLMs Done Right

California employers are hiring developers who can integrate AI into real products—reliably and at scale. This certification prepares you to design, build, evaluate, and deploy production-grade LLM-powered features.

We've designed the fully online Applied AI Software Engineering Certificate to help developers, managers, and computer science majors understand fundamental architecture and request-response flows in systems where LLM is a component. 

AI is no longer experimental. It's infrastructure.

Behind every AI feature is a team of engineers who design, build, 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:

• Model Context Protocol (MCP)
• Retrieval-augmented generation (RAG)
• Tool use and orchestration
• APIs to control and format input/output to/from LLMs
• 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, and scalability. 

Employers are seeking developers who can integrate LLM APIs into production systems, architect cloud-native AI applications, 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 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
• Engineering managers who design and review code and manage engineers that integrate with LLMs

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. 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 certificate
✔ Fees: $450/course. Total cost for the certification: $2,700.*

Courses & Dates for '26-'27

Go beyond any single LLM. The concepts, skills, and hands-on experience you’ll gain in this certificate are built to work across LLMs, keeping you flexible as the field evolves. You’ll become a faster, more confident developer ready to build!

Course 1: System Architecture & Request-Response Flows

In this course, you’ll learn how LLMs fit into client-server systems, how request–response flows work, and how to manage message history when interacting with LLMs via APIs. You’ll also use response streaming to deliver more responsive user experiences.

Dates: October 5–23
Registration: Opens September 1

Course 2: System Prompt as a Code Parameter

In this course, you’ll learn how to (re)use system prompts in code to enable side-by-side evaluation and rapid refinement. You’ll sharpen prompt effectiveness using deterministic techniques to control and shape LLM responses, work with APIs to send inputs, handle file uploads/downloads, and optimize performance.

Dates: November 2–20
Registration: Opens September 1

Course 3: Tool Use

In this course, you’ll learn tool-use request–response flows and to combine tool use with streaming to create responsive experiences. You’ll build custom tools LLMs can call and learn to handle tool-use errors effectively.

Dates: November 30–December 18
Registration: Opens September 1

Course 4: MCP (Model Context Protocol) Client & Server

In this course, you’ll learn MCP architecture, server deployment patterns, and request–response flows, and then build an MCP client and a tool-enabled MCP server.

Dates: January 25–February 12
Registration: Opens November 1

Course 5: AI Agents

In this course, you’ll learn agent architecture, types, workflows, and skills, then build a tool-using agent. You’ll also learn best practices for agent governance and understand how skills, tools, and MCPs fit together.

Dates: February 22–March 12
Registration: Opens November 1

Course 6: Retrieval-Augmented Generation (RAG)

In this course, you’ll learn RAG architecture and flows, understand text chunking and embedding, and build a RAG pipeline.

Dates: March 22–April 9
Registration: Opens November 1

Instructor

Courses are taught by Neerja Bhatnagar, a graduate of the MS in Computer Science program from California State University, Chico, and a software engineer with nearly 19 years of experience building complex systems, including 9 years building large-scale backend systems at Apple. She is now a founding member of algedonic.ai, where she will help build a reliable AI agent governance platform.

Requirements

This certificate uses Java for code walkthroughs and labs, but no prior Java or object-oriented experience is required—any programming background will do. We keep Java in the examples simple and approachable, avoiding advanced concepts so you can move fast and build real skills. Most labs start from working code, so you'll jump straight into hands-on practice instead of building from scratch. We use Postgres with code provided for setup and queries. A basic understanding of core computer science concepts, like client–server systems, request–response flows, and caching, is recommended. No machine learning background or knowledge of LLM internals is required.