One search. Connected ideas.

These platforms help collect traces, datasets, scores, and experiment results. LangSmith integrates closely with the LangChain ecosystem; Langfuse provides AI observability and evaluation workflows; Phoenix supports tracing and evaluation analysis. They are…

LLM-as-a-judge uses a model to grade an answer against a rubric. Human evaluation uses reviewers with defined criteria. Both produce judgments that need calibration and can have bias or inconsistency.

RAG evaluation measures retrieval quality and answer quality independently and together. Fine-tuning changes model behavior through training; long context supplies a large body of content directly; traditional search returns evidence without generating an…

Dataset preparation converts representative examples into the selected training format with reviewed expected outputs. Evaluation compares trained behavior against the baseline on separate data. RAG supplies current external knowledge at runtime; fine-tuning…

A production reference design specifies failed and uncertain outcomes as carefully as successful ones. Evaluation checks behavior before release; observability detects failures in actual runs. Human escalation is an explicit supported outcome.

Evaluate no evidence, stale policies, malformed documents, ambiguous dates, denied access, injected content, restarts, duplicate submissions, and uncertain writes. Track source/model/prompt/graph versions, p95 latency, tokens, cost per successful task,…

Beginner — Why isn't normal unit testing enough?

Semantic Kernel is a Microsoft ecosystem for integrating models, plugins/functions, and agent capabilities into applications. AutoGen is known for multi-agent conversation and orchestration abstractions. Their historical APIs remain relevant in existing…

A golden dataset is a curated set of representative inputs with expected outcomes, reference evidence, and scoring criteria. Regression testing reruns these cases after changes. AI tests often allow multiple acceptable phrasings while requiring the same…

Precision measures how many retrieved items are relevant. Recall measures how much relevant evidence was retrieved. Relevance indicates usefulness for the query; ranking metrics also consider whether good evidence appears early enough to be used.

Faithfulness asks whether the answer is supported by supplied evidence. Correctness asks whether it is right for the task and real applicable facts. Citation quality checks whether references point to supporting sources, not merely existing documents.

Smaller models often reduce cost/latency but may perform worse on complex tasks; stronger models may justify higher cost for difficult reasoning or synthesis. Routing sends different tasks to appropriate paths. The right split comes from evaluation, not model…

These platforms help collect traces, datasets, scores, and experiment results. LangSmith integrates closely with the LangChain ecosystem; Langfuse provides AI observability and evaluation workflows; Phoenix supports tracing and evaluation analysis. They are…

LLM-as-a-judge uses a model to grade an answer against a rubric. Human evaluation uses reviewers with defined criteria. Both produce judgments that need calibration and can have bias or inconsistency.

A golden dataset is a curated set of representative inputs with expected outcomes, reference evidence, and scoring criteria. Regression testing reruns these cases after changes. AI tests often allow multiple acceptable phrasings while requiring the same…

Precision measures how many retrieved items are relevant. Recall measures how much relevant evidence was retrieved. Relevance indicates usefulness for the query; ranking metrics also consider whether good evidence appears early enough to be used.

Faithfulness asks whether the answer is supported by supplied evidence. Correctness asks whether it is right for the task and real applicable facts. Citation quality checks whether references point to supporting sources, not merely existing documents.

RAG evaluation measures retrieval quality and answer quality independently and together. Fine-tuning changes model behavior through training; long context supplies a large body of content directly; traditional search returns evidence without generating an…

Semantic Kernel is a Microsoft ecosystem for integrating models, plugins/functions, and agent capabilities into applications. AutoGen is known for multi-agent conversation and orchestration abstractions. Their historical APIs remain relevant in existing…

Smaller models often reduce cost/latency but may perform worse on complex tasks; stronger models may justify higher cost for difficult reasoning or synthesis. Routing sends different tasks to appropriate paths. The right split comes from evaluation, not model…

Dataset preparation converts representative examples into the selected training format with reviewed expected outputs. Evaluation compares trained behavior against the baseline on separate data. RAG supplies current external knowledge at runtime; fine-tuning…

Model outputs vary and can be plausible but wrong. I still unit-test deterministic code, but also evaluate representative tasks, support from evidence, tool behavior, and safe failure using versioned datasets and rubrics.

NumPy provides efficient numerical arrays and vector operations. Pandas provides table-like data structures for cleaning and analyzing datasets. Jupyter provides interactive notebooks that mix code, results, and explanations.

Reflection revises an output after examining it. A critic is a separate checking role that evaluates an output against criteria. Either can be model-based, rule-based, or a combination.

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module

AI Engineering learning module