Test AI & LLM App with DeepEval, RAGAs & more using Ollama

Roadmap to become AI QA Engineer to test LLMs and AI Application using DeepEval, RAGAs and HF Evaluate with Local LLMs

What you'll learn
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

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

Description
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).

Who this course is for:
QA Engineers, AI QA Test Engineers, Business Analyst, AI Engineers