What is the Build With LLMs program from Carnegie Mellon University?

This is a live online certificate program from CMU’s School of Computer Science Executive and Professional Education that trains technical professionals to design and deploy LLM-powered AI systems. The curriculum spans from foundational LLM concepts through context engineering and prompting to advanced system design (RAG pipelines, tool integration, and multi-agent workflows).

Who should enroll and what are the prerequisites?

The program is targeted at experienced technical professionals (such as AI/ML engineers, software developers, data scientists, MLOps engineers, etc.) who work with or alongside AI systems. Participants are expected to have a working knowledge of Python programming, algorithms/data structures, and basic AI/LLM concepts. No prior experience building LLM agents is required, but familiarity with coding and AI fundamentals will help you keep pace with the hands-on labs and projects.

What topics and skills does the program cover?

The curriculum covers the full LLM engineering lifecycle. It begins with LLM foundations (transformers, attention, model scaling) and moves into context engineering (prompt design, in-context learning, chain-of-thought reasoning). It then covers Retrieval-Augmented Generation (RAG) – building retrieval pipelines with embeddings and vector databases – as well as tool integration (the Model Context Protocol for LLMs to call external APIs). Finally it covers agentic AI, teaching how to design and coordinate both single-agent and multi-agent systems. Along the way, the program also teaches how to evaluate LLM behavior for reliability (handling hallucinations, bias, security) and implement guardrails.

How does this program differ from other AI/LLM courses?

Unlike introductory AI or LLM courses that focus on individual models or prompts, this program emphasizes system-level design. It teaches you to build complete LLM-based workflows and agents – handling architecture, multi-step planning, and evaluation – rather than just one-off prompts.

Large Language Model Certificate Program

4.5 Rated

200+ Learners

Program Fees
$4,000
As low as $400/month
Application closes on
26 Sep, 2026
Program Duration
8 weeks 4-6 hours/week
Learning Format
Live, Online, Interactive

Why Join this Program

Recognized Credential
Earn a certificate of completion from CMU School of Computer Science Executive Education

Earn a certificate of completion from CMU School of Computer Science Executive Education
End-to-End System Design
Progress from LLM foundations to RAG, tool calling, and multi-agent orchestration
Applied LLM engineering
Apply every concept through guided virtual labs, real assignments, and project work
Portfolio-Ready Outcome
Leave with a working, evaluated AI system you can showcase to prospective employers

Corporate Training

Enroll your employees into this program, NOW!

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Program Overview

Build the engineering skills to design modern LLM applications. Explore context engineering, prompting, reasoning, Retrieval-Augmented Generation (RAG), tool calling, Agentic AI, and AI safety while developing production-oriented systems through applied learning.

Key Features

Build LLM applications using advanced context, retrieval, and reasoning techniques
Improve LLM performance through context engineering and in-context learning
Develop RAG systems that ground LLMs in trusted knowledge and enterprise data
Integrate LLMs with tools, APIs, and enterprise systems through MCP
Design multi-agent systems for complex planning, coordination, and execution
Evaluate LLMs across reliability, safety, hallucinations, bias, and security
Explore self-attention, BERT, in-context learning, and modern training approaches
Experience hands-on learning through 3 course-end projects, 4 capstones, and 3 case studies
Learn through a curriculum developed and delivered by CMU School of Computer Science faculty

Program Advantage

Upon successful completion, earn a digital certificate from Carnegie Mellon University School of Computer Science Executive Education - validating your expertise in building production-grade LLM and multi-agent systems.

Illustrative certificate only
The U.S. News & World Report Ranks CMU
- #1 in Artificial Intelligence programs
- #1 in Programming Language and Systems
- #1 for overall graduate computer science programs

Program Details

Take your LLM skills from foundations to building reliable AI applications. Explore context engineering, reasoning, RAG, tool integration, multi-agent systems, and AI safety while developing and evaluating real-world LLM solutions.

Learning Path

Program Induction - Build With LLMs
You will be introduced to the program and learning journey through a program overview.
Foundations of Large Language Models
- Evolution: embeddings → transformers → BERT/GPT/T5 → GPT-3
- Pre-training data, corpora & data controversies
- Benchmarks, contamination, scaling laws, emergent abilities
- Modern landscape: multimodal, reasoning, small & open models, MoE
- Instruction tuning essentials (FLAN, Self-Instruct — condensed)
In-context Learning and Reasoning - CoT to Reasoning Models
- Prompt engineering fundamentals primer (beginner on-ramp)
- ICL: instructions, templates, demonstrations, selection & order
- Prompt sensitivity, biases, calibration, experiment design
- Few-shot & zero-shot CoT, triggers, self-consistency, Auto-CoT
- CoT limits & faithfulness
- Self-Ask, Plan-and-Solve, Step-Back, Tree/Graph-of-Thoughts
- How reasoning models (o1/R1-class) are trained; overthinking
Augmentation: RAG, Tool Calling and MCP
- RAG fundamentals: chunking, vectorization, similarity
- RAG limits: lost-in-the-middle, RAG vs long-context
- Advanced chunking: semantic, recursive, hierarchical, context-aware
- Embeddings & vector databases in practice (ChromaDB/FAISS)
- Tool-use foundations: Toolformer, ART, agent tool loop
- Tool evaluation: ToolBench, BFCL, tool-selection strategies
- Function calling mechanics (JSON schemas, worked example)
- Web agents: WebGPT, WebArena
- Model Context Protocol (MCP) — architecture, servers, integration
Multi-Stage Pipelines & Self-Verification
- Task decomposition & prompt chaining (DecomP, Least-to-Most)
- LangChain introduction
- Auto-prompting (APE), self-correction pitfalls, Chain-of-Verification
- LLM-as-a-Judge evaluation & its biases
Agentic AI - Agents, Memory & Collaboration
- Agent fundamentals: ReAct, Reflexion, TAO loop
- Agent memory systems & frameworks (AutoGen, AgentKit)
- Planning: why LLMs struggle, LLM-Modulo, TravelPlanner (condensed)
- Multi-agent collaboration & debate, Mixture-of-Agents
- Agent failure modes (lost-in-conversation, MAS failures)
- Modern agent frameworks survey (LangGraph, CrewAI, etc.
Reliability & Alignment
- Hallucinations: types, sources, calibration, mitigation
- Bias, toxicity, fairness metrics & detoxification
- Sycophancy & RLHF/InstructGPT pipeline
- Ethics, deception & Sleeper Agents (condensed highlights)
LLM Security
- Prompt injection (direct/indirect) & attack mechanics
- Jailbreaking families & real-world attack case studies
- Defenses: guard models, firewalls, detection
- OWASP LLM Top 10, data poisoning, model theft
- Production guardrails implementation lab (NeMo/LlamaGuard hands-on)
AI for Software Engineering
- Code generation & evaluation (pass@k, HumanEval — condensed)
- SWE agents: SWE-bench, SWE-agent, OpenHands, Agentless
- Claude Code architecture & spec-driven development (SpecKit)
- Velocity vs tech debt; production incidents
Build with LLMs Capstone Project
- Build and evaluate an end-to-end agentic LLM application.
- Design an agentic system combining RAG and MCP-based tool integrations
- Apply task decomposition, self-reflection, and agent orchestration
- Evaluate faithfulness, tool correctness, sycophancy, and jailbreak resistance
- Present your solution with a structured evaluation and mitigation report

Programme Syllabus

13+ Skills Covered

Large Language Models
Context Engineering
Prompt Engineering
In Context Learning
Chain of Thought Prompting
Retrieval Augmented Generation
Embeddings
Vector Databases
Tool Calling
Multi Agent Systems
AI Safety
LLM Evaluation
Model Context Protocol

17+ Tools Covered

Projects Covered

Project 1
MCP Powered Order Assistant with Human Approval
Build an MCP server that connects an AI assistant to order, returns and shipping tools, with human approval, audit logging and failure handling.
Project 2
Multi Agent RFP Response System
Orchestrate three specialized agents to extract RFP requirements, draft evidence-based responses and red-team claims using LangGraph or CrewAI.
Project 3
Secure an AI Copilot Through Red Teaming
Red-team an AI copilot using prompt injection, strengthen it with guardrails, and measure improvements in its resilience through before-and-after security scans.
Project 4
Secure RAG Powered AI Agent
Develop a RAG agent that answers from trusted documents, then test it against prompt injection and strengthen its resilience with security guardrails.
Project 5
AI Assistant for Small Business Support
Create an AI assistant grounded in SBA and IRS guidance that answers business queries and takes action through an appointment-booking tool.
Project 6
AI Powered IT and HR Helpdesk
Design an internal helpdesk assistant that retrieves relevant information, answers employee queries and creates support tickets with human approval.
Project 7
AI Assistant for Campus Policy Support
Develop an AI assistant grounded in CMU academic policies to provide reliable, policy-based answers and support informed decision-making.
Project 8
Advanced Context Engineering for Coding Agents
Apply context engineering to help coding agents tackle complex production codebases using compaction, sub-agents, re-steering, and structured research and implementation workflows.
Project 9
Context Engineering for Effective AI Agents
Explore how to manage context for AI agents through system prompts, tool selection, retrieval, compaction, and long-horizon memory to improve agent reliability.
Project 10
Securing AI Agents in Production Lessons from Replit
Examine the Replit database incident and identify controls for safer AI agents, including IAM restrictions, approval gates, sandboxing, backups, monitoring, and policy-as-code.

Disclaimer - The projects have been built leveraging real publicly available datasets from organizations.

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Program Advisors

Travis D. Breaux
Associate Professor of Computer Science, Carnegie Mellon University
A leading researcher in AI, privacy, and software engineering, Professor Travis D. Breaux specializes in trustworthy AI, LLM-powered systems, and secure, policy-compliant software design.

The CMU Advantage

A Global Leader in AI, Computing, and Innovation

AI program and Computer Science program in the U.S.

Source: U.S. News

Rank in the in Most Innovative Schools

Source: U.S. News

Turing Awards recognizing excellence in computer science.

Source: CMU

Companies Hiring

Batch Profile

This program caters to working professionals from a variety of industries and backgrounds; the diversity of our students adds richness to class discussions and interactions.

Industry
The class consists of learners from excellent organizations and diverse industries
Industry
IT & Software - 34%AI & ML - 24%SaaS & Cloud - 18%Consulting - 14%BFSI - 10%
Companies

Admission Details

Eligibility Criteria

Designed for technical professionals looking to build production-ready LLM systems.

Functional knowledge of Python programming

Familiarity with machine learning and LLM concepts

3+ years of professional experience in IT or technology roles

Admission Fee & Financing

The admission fee for this program is $4,000

Financing Options

We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.

Total Program Fee

$4,000

11% off

$4,500

Pay In Installments, as low as

$400/month

You can pay monthly installments using our payment partners with low APR and no hidden fees.

Program Cohorts

Next Cohort

CMU Build With LLMs: From Context Engineering to Multi-Agent Systems Nov 2026
- Date
  Time
  Batch Type
- Program Induction
  28 Oct, 2026
  10:00 CDT
- Regular Classes
  5 Nov, 2026 - 11 Feb, 2027
  17:00 - 18:30 CST
  WeekdayMTuWThFSaSu

Got Questions Regarding Cohort Dates?

FAQs

What is the Build With LLMs program from Carnegie Mellon University?
This is a live online certificate program from CMU’s School of Computer Science Executive and Professional Education that trains technical professionals to design and deploy LLM-powered AI systems.

The curriculum spans from foundational LLM concepts through context engineering and prompting to advanced system design (RAG pipelines, tool integration, and multi-agent workflows).
Who should enroll and what are the prerequisites?
The program is targeted at experienced technical professionals (such as AI/ML engineers, software developers, data scientists, MLOps engineers, etc.) who work with or alongside AI systems.

Participants are expected to have a working knowledge of Python programming, algorithms/data structures, and basic AI/LLM concepts. No prior experience building LLM agents is required, but familiarity with coding and AI fundamentals will help you keep pace with the hands-on labs and projects.
What topics and skills does the program cover?
The curriculum covers the full LLM engineering lifecycle. It begins with LLM foundations (transformers, attention, model scaling) and moves into context engineering (prompt design, in-context learning, chain-of-thought reasoning).

It then covers Retrieval-Augmented Generation (RAG) – building retrieval pipelines with embeddings and vector databases – as well as tool integration (the Model Context Protocol for LLMs to call external APIs).

Finally it covers agentic AI, teaching how to design and coordinate both single-agent and multi-agent systems. Along the way, the program also teaches how to evaluate LLM behavior for reliability (handling hallucinations, bias, security) and implement guardrails.
How does this program differ from other AI/LLM courses?
Unlike introductory AI or LLM courses that focus on individual models or prompts, this program emphasizes system-level design. It teaches you to build complete LLM-based workflows and agents – handling architecture, multi-step planning, and evaluation – rather than just one-off prompts.