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A practical guide on writing effective evaluations for AI agents, focusing on starting from observed failures and using a mix of deterministic checks and LLM judges.
This article provides a practical guide on running 8x NVIDIA RTX PRO 6000 Blackwell GPUs for AI workloads, emphasizing high-concurrency inference, model fleets, and 70B fine-tuning, while comparing performance to more expensive data center solutions.
A practical guide by Burke Holland on using GitHub Copilot effectively with a simple, repeatable workflow, emphasizing the default harness over chasing new AI tools.
A practical guide to building AI agents using LLMs, RAG, and knowledge graphs, available on Amazon.
This guide explains how to evaluate the quality of AI-generated code using tests, golden datasets, reliability checks, and human review. It provides a practical workflow for catching regressions and shipping AI-assisted code with more confidence.
A practical guide on LLM design patterns covering data preparation, model development, fine-tuning, RAG, and advanced prompting techniques.
A conceptual guide on deciding when to trust LLM outputs in high-stakes professional contexts like legal, clinical, and financial work, emphasizing the need for critical evaluation skills.
A practitioner shares 20 real AI agents for sales, operations, content, dev, and finance that are actively used and have survived the first week, emphasizing single-job agents with approval gates and structured output.
Recommends a Stanford lecture on how ChatGPT and Claude work, distilling its core insights into a practical guide to help users effectively use AI tools.