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Testing LLMs: Why It Matters, Common Data Challenges, and Proven Testing Strategies
Testing LLMs: Why It Matters, Common Data Challenges, and Proven Testing Strategies
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- Testing LLMs: Why It Matters, Common Data Challenges, and Proven Testing Strategies
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About The Video
Large Language Models (LLMs) are all the rage in 2025 - watch this episode of XP Series featuring 𝐕𝐲𝐚𝐬𝐚𝐫𝐚𝐣 𝐏𝐚𝐝𝐚𝐤𝐚𝐧𝐝𝐥𝐚, Practice Head - Digital Assurance, Canarys.
🎙️In this episode, Vyasaraj delves into the critical role of testing Large Language Models (LLMs) to ensure reliable and ethical AI solutions. He discusses key testing strategies like functional, adversarial, and stress testing to address challenges such as biases, inaccuracies, and scalability.
Gain valuable insights into creating robust LLM testing strategies, managing biases, and addressing the challenges unique to AI-driven models. Whether you're just starting or looking to refine your practices, this session provides actionable knowledge for all AI practitioners.
Video Chapters
00:00 Welcome
01:01 Guest Introduction
02:37 Differences between LLM and Traditional Testing
07:22 Framework for LLM Testing Strategies
12:39 How Often Should LLMs Be Retested After Deployment
15:43 Mitigating Bias in LLM Datasets
17:50 Test Automation in LLM
20:34 Designing Testing Protocols for Ethical LLM Outputs
22:50 Aligning LLM Testing with AI Regulations
26:05 Team Structure for LLM Testing
29:01 Final Advice for Implementing LLM Testing
30:34 Wrapping Up
Key Topics Covered
Importance of Testing LLMs
Emphasizes the necessity of rigorous testing to ensure LLMs perform as expected across various applications.
Differences Between LLM and Traditional Testing
Discusses how LLM testing differs from traditional software testing, highlighting the unique challenges posed by LLMs.
Framework for LLM Testing Strategies
Introduces a structured approach to testing LLMs, focusing on strategies to assess their performance effectively.
Frequency of Retesting LLMs Post-Deployment
Addresses the importance of continuous testing and the need to periodically retest LLMs after deployment to maintain their reliability.
Mitigating Bias in LLM Datasets
Explores methods to identify and reduce biases in the datasets used to train LLMs, ensuring fair and equitable outcomes.
Test Automation in LLM
Highlights the role of automation in testing LLMs, aiming to improve efficiency and consistency in the testing process.
Designing Testing Protocols for Ethical LLM Outputs
Discusses the creation of testing protocols that ensure LLMs produce ethical and responsible outputs.
Aligning LLM Testing with AI Regulations
Examines how LLM testing can be aligned with existing and emerging AI regulations to ensure compliance and ethical standards.
Team Structure for LLM Testing
Provides insights into the organizational structure necessary for effective LLM testing, including roles and responsibilities.
Related Blogs & Hubs
Artificial Intelligence (AI) in Software Engineering
AI-Powered QA: How Large Language Models Are Revolutionizing Software Testing- Part 1
LambdaTest
LambdaTest is an AI-Native test orchestration and execution platform that allows you to perform both manual and automated testing across 3000+ environments, making it a top choice among other cloud testing platforms.
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