Name: Test AI & LLM App with DeepEval, RAGAs & more using Ollama
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Erstellt vonKarthik KK

Zuletzt aktualisiert 5/2026

Englisch

Das wirst du lernen

Understand the purpose of Testing LLM and LLM based Application
Understand DeepEval and RAGAs in detail from complete ground up
Understand different metrics and evaluations to evaluate LLMs and LLM based app using DeepEval and RAGAs
Understand the advanced concepts of DeepEval and RAGAs
Testing RAG based application using DeepEval and RAGAs
Testing AI Agents using DeepEval to understand how tool callings can be tested

Kursinhalt

15 Abschnitte • 103 Lektionen • 10 Std. 33 Min. Gesamtdauer

Introduction to Testing of LLM Applications9:11
Understanding different types of AI Applications13:09
Basics of LLMs Evaluation and understanding Types of Evaluations10:03
Working with Human Based (Graded) Evaluation17:17
Understanding different Evaluation Metrics to evaluate AI Applications5:48
Different LLM libraries to Evaluate LLMs and LLM Applications8:03
Check your knowledge!

Testing LLMs with Traditional Approach5:21
Testing/Evaluating LLMs with Non Traditional Approach4:04
LLMs-As-Judge while Evaluating LLMs and LLMs Based Applications5:40
Writing First DeepEval Code for AnswerRelevance7:47
Testing LLMs for Context Precision12:45
Evaluating LLMs and check the Evaluated Testcases in Confident AI10:59
Evaluating Multiple Test Cases for Answer Relevance Metrics8:20
Understanding Dataset and creating Dataset with EvaluateDataSet9:45
Using Golden Dataset for Evaluating7:52
Creating Golden Dataset and Pushing it to Confident AI12:58
Evaluating Testcases from Golden Dataset by Pulling Dataset from Confident AI12:02
COMMAND UPDATED FROM DEEPEVAL ⚡️0:26
Using Local DeepSeek R1 LLMs Model for Evaluating using Ollama4:21
Running Evaluation using Local LLMs being LLMs-as-Judge4:25
Check your knowledge!
Deepen your understanding of the basic concepts of DeepEval with AI Assistant!

Introduction4:13
Using LangChain to Invoke LLM for our Test Source6:49
Evaluating LLM with Answer Relevancy using Local LLM9:58
Evaluating LLM with Context Precision and controlling Threshold using Local LLM8:32
Evaluating LLM with Bias using Local LLMs5:01
Using GEval Framework and Create Custom Bias Metrics to Evaluate LLM7:29
Using GEval to perform Bias Testing using Local LLMs5:07
Check your knowledge!

Anforderungen

Basics of working with LLM like using ChatGPT
Basics of any programing language like Java or Javascript
Basics of python will be a plus

Beschreibung

Testing AI & LLM App with DeepEval, RAGAs & more using Ollama and Local Large Language Models (LLMs)

Master the essential skills for testing and evaluating AI applications, particularly Large Language Models (LLMs). This hands-on course equips QA, AI QA, Developers, data scientists, and AI practitioners with cutting-edge techniques to assess AI performance, identify biases, and ensure robust application development.

Topics Covered:

Section 1: Foundations of AI Application Testing (Introduction to LLM testing, AI application types, evaluation metrics, LLM evaluation libraries).
Section 2: Local LLM Deployment with Ollama (Local LLM deployment, AI models, running LLMs locally, Ollama implementation, GUI/CLI, setting up Ollama as API).
Section 3: Environment Setup (Jupyter Notebook for tests, setting up Confident AI).
Section 4: DeepEval Basics (Traditional LLM testing, first DeepEval code for AnswerRelevance, Context Precision, evaluating in Confident AI, testing with local LLM, understanding LLMTestCases and Goldens).
Section 5: Advanced LLM Evaluation (LangChain for LLMs, evaluating Answer Relevancy, Context Precision, bias detection, custom criteria with GEval, advanced bias testing).
Section 6: RAG Testing with DeepEval (Introduction to RAG, understanding RAG apps, demo, creating GEval for RAG, testing for conciseness & completeness).
Section 7: Advanced RAG Testing with DeepEval (Creating multiple test data, Goldens in Confident AI, actual output and retrieval context, LLMTestCases from dataset, running evaluation for RAG).
Section 8: Testing AI Agents and Tool Callings (Understanding AI Agents, working with agents, testing agents with and without actual systems, testing with multiple datasets).
Section 9: Evaluating LLMs using RAGAS (Introduction to RAGAS, Context Recall, Noise Sensitivity, MultiTurnSample, general purpose metrics for summaries and harmfulness).
Section 10: Testing RAG applications with RAGAS (Introduction and setup, creating retrievers and vector stores, MultiTurnSample dataset for RAG, evaluating RAG with RAGAS).

Für wen eignet sich dieser Kurs:

QA Engineers
AI QA Test Engineers
Business Analyst
AI Engineers

Das wirst du lernen

Zugehörige Themengebiete entdecken

Kursinhalt

Introduction6 Lektionen • 1 Std. 4 Min.

Running LLM locally using Ollama6 Lektionen • 26 Min.

Complete course Source code1 Lektionen • 1 Min.

Environment step required for Testing/Evaluating LLM Apps and LLMs5 Lektionen • 21 Min.

Understanding the Basics of DeepEval (Building Blocks)15 Lektionen • 1 Std. 47 Min.

Evaluating Real LLMs (Locally) as the Source and creating Dataset with LLMs7 Lektionen • 47 Min.

Testing RAG (Retrieval-Augmented Generation) application using DeepEval6 Lektionen • 32 Min.

Testing RAG application with DeepEval (Advanced)8 Lektionen • 30 Min.

Testing AI Agents and Tool Callings with Local LLMs and DeepEval8 Lektionen • 41 Min.

Evaluating/Testing LLMs using RAGAs7 Lektionen • 42 Min.

Anforderungen

Beschreibung

Für wen eignet sich dieser Kurs: