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Surveys and analyses of modern AI system design, evaluation, and production deployment.
9 articles in this series
AI code review tools have gone from novelty to standard practice in serious engineering teams. After a year of production use, here is an honest accounting…
Moving beyond toy prompts to production-grade patterns: structured outputs, chain-of-thought, prompt versioning, and evaluation-driven iteration.
Treating prompts as code: version control, evaluation frameworks, structured outputs with Zod, and prompt injection defense in production systems.
A practical comparison of the three dominant vector database options, with benchmarks, tradeoffs, and guidance on choosing the right one for your stack.
Practical coordination patterns for multi-agent AI systems: token budgets, shared context, tool routing, loop prevention, and failure isolation.
A structured framework for choosing between fine-tuning and retrieval-augmented generation, based on what your system actually needs to do.
Shipping LLM features without an evaluation framework is flying blind. Here is how to build one that gives you real signal.
After running both systems in parallel on real workloads, the results challenge some widely-held assumptions about when graphs actually win.
When to use knowledge graphs over vector embeddings, how to build them from documents, and the GraphRAG patterns that combine both for hybrid retrieval.
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