Chico State Professional & Continuing Education

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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 AI Application Engineering Certification to help developers, managers, and computer science majors understand fundamental architecture and request-response flows in systems where LLM is a component. The fully online program integrates with busy schedules and supports your career advancement and skill growth with stackable, 1-unit courses focused on 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, 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 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 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.

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

· How to stream LLM responses for tool use

· A built-in tool example

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

Dates: Aug. 31–Sept. 18, 2026

Learn about MCP client and server, and how to leverage tool use with MCP. Learning outcomes:

· Learn MCP architecture and end-to-end request-response flow
· Learn step-by-step how to build an MCP client and server (tool use)

Course 5: AI 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: Retrieval-Augmented Generation (RAG)

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

Apply Today

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.