Coursera

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

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.

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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

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Taught in English
Recently updated!

March 2026

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

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

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: Microservices
Category: Scalability
Category: Open Source Technology
Category: AWS CloudFormation
Category: Kubernetes
Category: Managed Services
Category: Containerization
Category: Azure DevOps
Category: LLM Application
Category: Application Deployment
Category: Software Architecture
Category: Application Performance Management
Category: Infrastructure Architecture
Category: Data Pipelines
Category: Apache Airflow
Category: Large Language Modeling
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: Operational Efficiency
Category: User Acceptance Testing (UAT)
Category: Key Performance Indicators (KPIs)
Category: Standard Operating Procedure
Category: Vector Databases
Category: MLOps (Machine Learning Operations)
Category: Large Language Modeling
Category: Cost Reduction
Category: Cost Management
Category: Process Mapping
Category: Prompt Engineering
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Prompt Patterns
Category: Process Optimization
Category: Workflow Management
Category: LLM Application
Category: Product Requirements
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: Model Evaluation
Category: Python Programming
Category: SQL
Category: Data Ethics
Category: Technical Communication
Category: LLM Application
Category: Technical Design
Category: AWS CloudFormation
Category: CI/CD
Category: System Design and Implementation
Category: Responsible AI
Category: AI Product Strategy
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: AI Workflows
Category: MLOps (Machine Learning Operations)
Category: AI Security
Category: Apache Airflow
Category: Model Deployment
Category: Prompt Engineering
Category: Communication

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Professionals from the Industry
265 Courses 37,951 learners

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