Coursera

LLM Engineering That Works: Prompting, Tuning, and Retrieval Professional Certificate

Coursera

LLM Engineering That Works: Prompting, Tuning, and Retrieval Professional Certificate

Engineer Production-Ready LLM Systems.

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

Access provided by SGCSRC

Earn a career credential that demonstrates your expertise
Intermediate level

Recommended experience

2 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Earn a career credential that demonstrates your expertise
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

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Taught in English
Recently updated!

March 2026

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Professional Certificate - 6 course series

Production AI Model Development and Ethics

Production AI Model Development and Ethics

Course 1, 10 hours

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 Deployment
Category: Feature Engineering
Category: Technical Documentation
Category: Model Training
Category: Model Evaluation
Category: Responsible AI
Category: Software Documentation
Category: Scikit Learn (Machine Learning Library)
Category: Data Pipelines
Category: Data Ethics
Category: Model Optimization
Category: Deep Learning
Category: PyTorch (Machine Learning Library)
Category: Data Preprocessing
Category: MLOps (Machine Learning Operations)
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: Model Evaluation
Category: SQL
Category: Performance Testing
Category: Performance Tuning
Category: Data-Driven Decision-Making
Category: Statistical Analysis
Category: Statistical Methods
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Debugging
Category: Query Languages
Category: Vector Databases
Category: Python Programming
Category: MLOps (Machine Learning Operations)
Category: LLM Application
Category: Large Language Modeling
Category: Retrieval-Augmented Generation
Category: Statistical Hypothesis Testing
Testing and Refining LLM Applications

Testing and Refining LLM Applications

Course 3, 13 hours

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: Software Testing
Category: MLOps (Machine Learning Operations)
Category: AI Security
Category: Test Driven Development (TDD)
Category: Continuous Integration
Category: Unit Testing
Category: Security Testing
Category: Model Deployment
Category: Statistical Analysis
Category: AI Workflows
Category: Test Script Development
Category: Test Automation
Category: SQL
Category: Python Programming
Category: LLM Application
Category: Test Case
Category: Responsible AI
Category: Large Language Modeling
Category: Testability
Category: CI/CD
Designing Production LLM Architectures

Designing Production LLM Architectures

Course 4, 11 hours

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 Deployment
Category: Scalability
Category: Cloud-Native Computing
Category: Microservices
Category: Kubernetes
Category: Apache Airflow
Category: Diagram Design
Category: Data Pipelines
Category: Software Architecture
Category: Large Language Modeling
Category: LLM Application
Category: Infrastructure Architecture
Category: Software Design
Category: AWS CloudFormation
Category: Azure DevOps
Category: Containerization
Category: Systems Architecture
Category: Model Deployment
Category: Managed Services
Category: Open Source Technology
Evaluating LLM Performance and Efficiency

Evaluating LLM Performance and Efficiency

Course 5, 9 hours

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: Product Requirements
Category: Prompt Engineering
Category: Process Optimization
Category: Model Optimization
Category: Product Lifecycle Management
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Process Mapping
Category: User Requirements Documents
Category: Product Management
Category: Prompt Patterns
Category: MLOps (Machine Learning Operations)
Category: Business Process Automation
Category: Operational Efficiency
Category: Process Design
Category: Large Language Modeling
Category: Key Performance Indicators (KPIs)
Category: LLM Application
Category: Process Driven Development
Category: Cost Containment
Category: Token Optimization
Advancing Your Career in Production AI

Advancing Your Career in Production AI

Course 6, 1 hour

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

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471 Courses78,263 learners

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