LLM Evaluation Courses and Certifications

Coursera Career Certificate

LLM Engineering That Works: Prompting, Tuning, and Retrieval

Build production-grade LLM applications end to end: prompt design, model tuning, retrieval, evaluation, deployment, and monitoring of reliable, ethical, cost-efficient AI systems.

• Coursera
• 8 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 6 courses

Career Certificate

AWS Generative AI Developer Advanced

Build reliable enterprise generative AI on AWS, combining Amazon Bedrock architectures with agentic workflows, security governance, cost optimization, monitoring, and hallucination-aware evaluation.

• Coursera
• 4 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 22 courses

Edureka

Advanced Prompting and Context Engineering

Design advanced prompts for LLMs: chain-of-thought, tree-of-thought, prompt chaining, context engineering, RAG pipelines, A/B testing, and defenses against prompt injection.

• Coursera
• 5 weeks, 2 hours a week
• Self-Paced
• Paid Course

Board Infinity

MLOps and LLMOps: Deploying and Scaling AI in Production

Design, deploy, and scale production ML and LLM systems with experiment tracking, ML-specific CI/CD, Kubernetes serving, canary and blue-green rollouts, and drift monitoring.

• Coursera
• 4 weeks, 4 hours a week
• Self-Paced
• Paid Course

H2O.ai

H2O ai Large Language Models (LLMs) - Level 3

Build custom h2oGPT models, assess them with LLM EvalGPT and evaluation metrics, and navigate the H2O.ai generative AI ecosystem and its components.

• Coursera
• 2 hours
• Self-Paced
• Paid Course

Coursera

Fine-tuning Text Models with PEFT

Fine-tune large language models on consumer GPUs: implement QLoRA and LoRA, tune learning rates, batch sizes and rank, and evaluate with perplexity, BLEU, ROUGE and BERTScore.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Fine-Tune & Optimize Generative AI Models

Tune decoding strategies (temperature, top-k, beam search), evaluate outputs with BLEU, ROUGE and BERTScore, and apply LoRA parameter-efficient fine-tuning using Hugging Face Transformers and PEFT.

• Coursera
• 3 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Analyze Logs: Fix LLM Hallucinations

Diagnose LLM hallucinations from production logs: use Pandas to segment users by intent, compute 7-day retention, run root cause analysis, and write an engineering brief.

• Coursera
• 3 hours 37 minutes
• Self-Paced
• Paid Course

Quick Start Guide to Large Language Models (LLMs): Unit 3

Build custom multimodal models with cross-attention, align LLMs via reinforcement learning, fine-tune BERT and GPT-2 with mixed precision, and deploy using quantization and knowledge distillation.

• Coursera
• 8 hours 7 minutes
• Self-Paced
• Paid Course

Coursera

Testing and Refining LLM Applications

Harden LLM microservices for production: apply TDD and mutation testing, build pytest safety guardrail suites, version models with DVC, and track experiments in W&B.

• Coursera
• 5 weeks, 3 hours a week
• Self-Paced
• Paid Course

Board Infinity

RAG from Scratch: Build a Knowledge-Powered Chatbot

Build a production RAG chatbot: generate OpenAI embeddings, chunk PDFs and HTML, compare ChromaDB, FAISS and Pinecone, add hybrid search, reranking, RAGAS evaluation and deployment.

• Coursera
• 5 weeks, 2 hours a week
• Self-Paced
• Paid Course

Coursera

Develop and Evaluate LLM Features Effectively

Draft a Product Requirements Document for an HR chatbot, defining scope, MVP features and success metrics, then build a User Acceptance Testing plan catching functional gaps and dangerous edge cases.

• Coursera
• 2 weeks, 1 hour a week
• Self-Paced
• Paid Course

Pragmatic AI Labs

Deterministic LLM programming

Build production LLM systems on AWS Bedrock: configure knowledge bases with S3 for RAG, compile llama.cpp for local GGUF inference, and evaluate LLM quality.

• Coursera
• 4 weeks, 1 hour a week
• Self-Paced
• Paid Course

Career Certificate

Microsoft Generative AI Operations (GenAIOps)

Treat generative AI as an operational discipline: design GenAIOps operating models, optimize RAG pipelines, build CI/CD and observability, and govern costs and content safety.

• Coursera
• 4 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 5 courses

H2O.ai

H2O Gen AI Ecosystem Overview - Level 1

Build custom GPT models with h2oGPT and H2O LLM Studio, prepare data with LLM DataStudio, evaluate models with EvalGPT, and deploy AI web apps via GenAI AppStudio.

• Coursera
• 9 weeks, 1 hour a week
• Self-Paced
• Paid Course