How to test the ai llm judge - day 07
Evaluating the evaluator: why your LLM-as-a-judge is a system under test that needs its own validation.
Evaluating the evaluator: why your LLM-as-a-judge is a system under test that needs its own validation.
Evaluating the logic behind having an LLM judge another LLMâs output. Establishing a helpfulness rubric and confronting definitions.
Putting predictable constraints and standard expectations to the test, evaluating how the LLM judge grades standard formatting and basic rules.
You donât need thousands of test cases to begin. Five carefully chosen examples are more than enough to boot up your AI evaluation pipeline.
Increasing the complexity of prompt inputs to see how the LLM judge responds and analyzing its limits when prompt length grows.
Setting up a basic LLM evaluation only to find the initial results predictable, but discovering why simple tests are the best place to start.
Running the exact same prompt multiple times reveals the inherent variability in LLM responses, a core challenge in AI testing.
Written by - Millan Kaul
How three minor Docker configuration changes shaved 33% off my Jekyll build times, saving hours of developer wait time.
Learn how to run your first RAG pipeline locally with Ollama, Qdrant, Python, LangChain, guardrails, and RAGAS evals.
An ML pipeline turns raw data into deployable models through repeatable, automated steps that improve consistency, scale, and reliability.
AI agents do not become reliable because the model is bigger. They become reliable because the harness around the model is testable, observable, and continuo...
Here is what constitution.md is, and how developers, architects, and QA teams should use it in spec-driven development.
QA is not disappearing in the AI era. It is becoming the layer that makes AI outputs reliable, testable, and safe.
QA is not disappearing in the AI era. It is becoming the layer that makes AI outputs reliable, testable, and safe.
Ask your AI to compare home loan rates, savings accounts, and credit cards instantly. Open Banking MCP brings Australian CDR data into Claude, Cursor, and VS...
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
âSingle LLMs hallucinate. Multiâagent systems multiply the problem.â
From hype to control. Here are the 5 truths every CTO must know.
âMCP handshake â tool call â your code runs. 3 minutes to understand.â
âLast week my LLM swore the 2024 World Cup winner was âMoon United FCâ. It was confident, detailed, and 100% hallucinated.â
MCP = LLM lifeline when models hallucinate most.
Model Context Protocol (MCP) = Standardized way for LLMs to discover and call your external tools/data.
Written by - Millan Kaul
Written by - Gavin Cheung
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Neelam Pal
Multiple : New Performance testing tool
Written by - Millan Kaul
{code}
Written by - Millan Kaul
Written by - Dennis Nyawira
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Written by - Millan Kaul
Written by - Alejandro Sanchez Giraldo , ChatGPT , Gith...
Written by - Millan Kaul
Written by - Alejandro Sanchez Giraldo , ChatGPT and Gi...
Written by - Alejandro Sanchez Giraldo , ChatGPT and Gi...
Written by - Karthik Hariharan
Written by - Millan Kaul
Written by - Alejandro Sanchez Giraldo and ChatGPT
Written by - Alejandro Sanchez Giraldo and ChatGPT
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Paul Maxwell-Walters
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
fetch()API in JavaScript
Written by - Millan Kaul
Written by - Millan Kaul
Welcome to #QualityWithMillan !
Evaluating the evaluator: why your LLM-as-a-judge is a system under test that needs its own validation.
Evaluating the logic behind having an LLM judge another LLMâs output. Establishing a helpfulness rubric and confronting definitions.
Putting predictable constraints and standard expectations to the test, evaluating how the LLM judge grades standard formatting and basic rules.
You donât need thousands of test cases to begin. Five carefully chosen examples are more than enough to boot up your AI evaluation pipeline.
Increasing the complexity of prompt inputs to see how the LLM judge responds and analyzing its limits when prompt length grows.
Setting up a basic LLM evaluation only to find the initial results predictable, but discovering why simple tests are the best place to start.
Running the exact same prompt multiple times reveals the inherent variability in LLM responses, a core challenge in AI testing.
An ML pipeline turns raw data into deployable models through repeatable, automated steps that improve consistency, scale, and reliability.
AI agents do not become reliable because the model is bigger. They become reliable because the harness around the model is testable, observable, and continuo...
Here is what constitution.md is, and how developers, architects, and QA teams should use it in spec-driven development.
QA is not disappearing in the AI era. It is becoming the layer that makes AI outputs reliable, testable, and safe.
QA is not disappearing in the AI era. It is becoming the layer that makes AI outputs reliable, testable, and safe.
Evaluating the evaluator: why your LLM-as-a-judge is a system under test that needs its own validation.
Evaluating the logic behind having an LLM judge another LLMâs output. Establishing a helpfulness rubric and confronting definitions.
Putting predictable constraints and standard expectations to the test, evaluating how the LLM judge grades standard formatting and basic rules.
You donât need thousands of test cases to begin. Five carefully chosen examples are more than enough to boot up your AI evaluation pipeline.
Increasing the complexity of prompt inputs to see how the LLM judge responds and analyzing its limits when prompt length grows.
Setting up a basic LLM evaluation only to find the initial results predictable, but discovering why simple tests are the best place to start.
Running the exact same prompt multiple times reveals the inherent variability in LLM responses, a core challenge in AI testing.
How three minor Docker configuration changes shaved 33% off my Jekyll build times, saving hours of developer wait time.
Learn how to run your first RAG pipeline locally with Ollama, Qdrant, Python, LangChain, guardrails, and RAGAS evals.
Written by - Millan Kaul
Written by - Millan Kaul
How Appium works
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
Learn how to run your first RAG pipeline locally with Ollama, Qdrant, Python, LangChain, guardrails, and RAGAS evals.
Ask your AI to compare home loan rates, savings accounts, and credit cards instantly. Open Banking MCP brings Australian CDR data into Claude, Cursor, and VS...
âSingle LLMs hallucinate. Multiâagent systems multiply the problem.â
âMCP handshake â tool call â your code runs. 3 minutes to understand.â
âLast week my LLM swore the 2024 World Cup winner was âMoon United FCâ. It was confident, detailed, and 100% hallucinated.â
MCP = LLM lifeline when models hallucinate most.
Model Context Protocol (MCP) = Standardized way for LLMs to discover and call your external tools/data.
âSingle LLMs hallucinate. Multiâagent systems multiply the problem.â
From hype to control. Here are the 5 truths every CTO must know.
âMCP handshake â tool call â your code runs. 3 minutes to understand.â
âLast week my LLM swore the 2024 World Cup winner was âMoon United FCâ. It was confident, detailed, and 100% hallucinated.â
MCP = LLM lifeline when models hallucinate most.
Model Context Protocol (MCP) = Standardized way for LLMs to discover and call your external tools/data.
An ML pipeline turns raw data into deployable models through repeatable, automated steps that improve consistency, scale, and reliability.
AI agents do not become reliable because the model is bigger. They become reliable because the harness around the model is testable, observable, and continuo...
Here is what constitution.md is, and how developers, architects, and QA teams should use it in spec-driven development.
QA is not disappearing in the AI era. It is becoming the layer that makes AI outputs reliable, testable, and safe.
QA is not disappearing in the AI era. It is becoming the layer that makes AI outputs reliable, testable, and safe.
Written by - Millan Kaul
Written by - Millan Kaul
Written by - Millan Kaul
The most successful QE leaders donât just enforce qualityâthey cultivate that environmentâŠ
Master the art of collaborating with multiple QA leads in enterprise projects. Learn effective strategies for test coordination, risk management and successf...
Multiple : New Performance testing tool
Written by - Millan Kaul
âMCP handshake â tool call â your code runs. 3 minutes to understand.â
From hype to control. Here are the 5 truths every CTO must know.
From hype to control. Here are the 5 truths every CTO must know.
âSingle LLMs hallucinate. Multiâagent systems multiply the problem.â
Written by - Millan Kaul
Ask your AI to compare home loan rates, savings accounts, and credit cards instantly. Open Banking MCP brings Australian CDR data into Claude, Cursor, and VS...