Coursera

LLM Engineering That Works: Prompting, Tuning, and Retrieval Specialization

Seize the savings! Get 40% off 3 months of Coursera Plus and full access to thousands of courses.

Coursera

LLM Engineering That Works: Prompting, Tuning, and Retrieval Specialization

Engineer Production-Ready LLM Systems.

Learn prompting, tuning, retrieval, and scalable architectures for reliable AI applications.

Included with Coursera Plus

Get in-depth knowledge of a subject
Intermediate level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Design and deploy production-grade LLM systems combining prompting, tuning, and retrieval

  • Build reliable, scalable AI pipelines with evaluation, monitoring, and governance

  • Apply responsible AI practices, ethics, and safety throughout the lifecycle of LLMs

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English
Recently updated!

March 2026

See how employees at top companies are mastering in-demand skills

 logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Coursera

Specialization - 6 course series

What you'll learn

  • Apply custom training loops with callbacks (early-stopping, checkpointing) and diagnose gradient issues using norm and activation analysis.

  • Implement feature engineering pipelines for structured and text data, then evaluate ML experiments to select production-ready models.

  • Create comprehensive model cards for LLM features that detail intended use, technical limitations, and specific fairness metrics.

  • Evaluate AI systems against established ethical guidelines to identify biases and propose actionable mitigation strategies.

Skills you'll gain

Category: Model Evaluation
Category: Machine Learning
Category: MLOps (Machine Learning Operations)
Category: Technical Documentation
Category: Deep Learning
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Model Deployment
Category: Software Documentation
Category: Data Pipelines
Category: Data Preprocessing
Category: Scalability
Category: Data Ethics
Category: Responsible AI
Category: Scikit Learn (Machine Learning Library)
Category: Feature Engineering
Category: PyTorch (Machine Learning Library)
Building Reliable LLM Systems

Building Reliable LLM Systems

Course 2 18 hours

What you'll learn

  • Build scripts with lexical/semantic metrics to evaluate LLMs, diagnose hallucinations, and balance vector-search recall against latency.

  • Apply hypothesis testing, confidence intervals, and significance metrics to evaluate model accuracy and validate results from A/B experiments.

  • Utilize parameterized SQL and data manipulation to segment user logs, calculate retention, and securely retrieve large-scale datasets.

  • Analyze LLM performance gaps to prioritize technical fixes and implement remediation measures for production-level reliability.

Skills you'll gain

Category: Large Language Modeling
Category: Performance Testing
Category: Vector Databases
Category: Python Programming
Category: Retrieval-Augmented Generation
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Statistical Hypothesis Testing
Category: Debugging
Category: SQL
Category: Performance Tuning
Category: MLOps (Machine Learning Operations)
Category: Data-Driven Decision-Making
Category: LLM Application
Category: Statistical Analysis
Category: Model Evaluation
Category: Pandas (Python Package)
Category: Query Languages

What you'll learn

  • Apply TDD to microservice endpoints and refactor modules based on code reviews to improve readability and reduce complexity.

  • Develop behavior and safety tests to ensure LLM outputs comply with policies and block unsafe changes to the model.

  • Apply data versioning to track artifacts and evaluate ML experiment runs to select production-ready models.

  • Create scripts using Python's argparse to automate multi-step computational workflows in cloud environments.

Skills you'll gain

Category: Unit Testing
Category: Statistical Analysis
Category: Testability
Category: Test Automation
Category: Large Language Modeling
Category: LLM Application
Category: Python Programming
Category: CI/CD
Category: AI Security
Category: Maintainability
Category: MLOps (Machine Learning Operations)
Category: Continuous Integration
Category: Code Coverage
Category: AI Workflows
Category: SQL
Category: Responsible AI
Category: Model Deployment
Category: Software Engineering
Category: Test Driven Development (TDD)
Category: Scripting

What you'll learn

  • Compare synchronous and asynchronous architectures and apply 12-factor principles and container orchestration to deploy scalable microservices.

  • Analyze multi-region deployments, pinpoint latency bottlenecks, and design resilient architecture improvements via fault analysis.

  • Create Airflow DAGs to automate data workflows and analyze the impact of schema evolution on downstream processes and tests.

  • Analyze trade-offs between self-hosting models vs. managed APIs and evaluate proposed infrastructure for fault tolerance and cost. 

Skills you'll gain

Category: Application Performance Management
Category: Infrastructure Architecture
Category: Open Source Technology
Category: Azure DevOps
Category: Managed Services
Category: Scalability
Category: Software Architecture
Category: Apache Airflow
Category: LLM Application
Category: Kubernetes
Category: Containerization
Category: Large Language Modeling
Category: Application Deployment
Category: Data Pipelines
Category: Microservices
Category: AWS CloudFormation
Category: Systems Architecture

What you'll learn

  • Create PRDs with requirements and success metrics, and evaluate features against user-story acceptance criteria to identify gaps.

  • Evaluate prompt patterns and compute-spend reports to implement model-optimization techniques that reduce operational costs.

  • Analyze pipelines using value-stream mapping to eliminate inefficiencies and prioritize chatbot KPI optimizations.

  • Create technical documentation for vector index updates and evaluate system effectiveness against business requirements.

Skills you'll gain

Category: Prompt Patterns
Category: Vector Databases
Category: Cost Reduction
Category: Operational Efficiency
Category: MLOps (Machine Learning Operations)
Category: Product Requirements
Category: Process Optimization
Category: Workflow Management
Category: Cost Management
Category: Standard Operating Procedure
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: LLM Application
Category: Process Mapping
Category: Large Language Modeling
Category: Key Performance Indicators (KPIs)
Category: Prompt Engineering
Category: User Acceptance Testing (UAT)
Category: Model Evaluation

What you'll learn

  • Position yourself for senior AI roles by creating a strategic portfolio and mastering advanced system design and ethics-focused technical interviews.

Skills you'll gain

Category: Technical Design
Category: Model Evaluation
Category: Communication
Category: CI/CD
Category: Python Programming
Category: AWS CloudFormation
Category: AI Security
Category: AI Workflows
Category: Apache Airflow
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Responsible AI
Category: AI Product Strategy
Category: SQL
Category: MLOps (Machine Learning Operations)
Category: Technical Communication
Category: LLM Application
Category: Prompt Engineering
Category: Data Ethics
Category: System Design and Implementation
Category: Model Deployment

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Professionals from the Industry
213 Courses 33,860 learners

Offered by

Coursera

Why people choose Coursera for their career

Felipe M.

Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Coursera Plus

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Frequently asked questions