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AI Evals in Practice. Testing, reliability, and quality for LLM systems in production Caio Incau

Książka w języku angielskim angielski
AI Evals in Practice. Testing, reliability, and quality for LLM systems in production Caio Incau - okladka książki

AI Evals in Practice. Testing, reliability, and quality for LLM systems in production Caio Incau - okladka książki

AI Evals in Practice. Testing, reliability, and quality for LLM systems in production Caio Incau - audiobook MP3

AI Evals in Practice. Testing, reliability, and quality for LLM systems in production Caio Incau - audiobook CD

Autor:
Caio Incau
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
216
Dostępne formaty:
     PDF
     ePub

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LLM applications can look healthy in dashboards while their answers quietly become less accurate, less useful, or less reliable. AI Evals in Practice gives developers and AI engineers a systematic way to measure quality, catch regressions, and make evidence-based improvements before and after deployment.
You will build evalkit, a complete Python evaluation harness, while learning the core components of an eval: datasets, scorers, runners, and golden test sets. You will create deterministic scorers and LLM-as-judge evaluations, then calibrate judges and mitigate common biases. The book applies these foundations to prompt regression testing and CI, RAG retrieval and generation metrics, agent trajectories and tool calls, and human annotation workflows. You will then extend evaluation into production with online sampling, guardrails, and cost- and latency-aware pipelines, while comparing tools such as DeepEval, promptfoo, Langfuse, and Braintrust. A case study and final project bring the pieces together into an eval-driven development workflow with CI and a dashboard.
By the end, you will be able to design evaluation pipelines that help you ship LLM systems with measurable, repeatable quality.

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