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23 articles tagged #LLM.
P.01Unit tests tell you if your code does what you wrote. They don't tell you if your AI feature does what users need. Here's how to build an evaluation pipeline that catches the failures that matter before users do.
P.02LLM calls are slow and expensive. Caching them is the obvious move. But caching the wrong responses breaks the user experience in ways that are subtle and hard to debug. Here's a practical guide to doing it right.
P.03Rolling back a bad API endpoint takes seconds. Rolling back a bad LLM integration is harder — the damage may already be in your logs, your users' inboxes, or your clients' feeds. Feature flags are how you ship AI features without betting everything on launch day.
P.04AI features ship fast. Then the monthly API bill arrives. Here's a systematic approach to understanding and reducing LLM costs without breaking the product.
P.05Getting a language model to return valid, schema-conforming JSON is harder than it looks. Here's what works in production, from native structured output APIs to library-level validation.
P.06Every AI integration budget starts with API costs and ends with surprises. Here's what production AI features actually cost once you account for everything the initial estimate missed.
P.07Prompt injection is the SQL injection of the AI era. As LLMs ship into production apps by the millions, attackers are learning how to hijack them through the data they consume. Here's what the attack looks like and how to defend against it.
P.08Explore ZeroDayBench—A new benchmark testing the efficacy of leading LLM agents in discovering and patching unseen security vulnerabilities.
P.09EditorPickPrompt engineering is dead. Context engineering -- managing system prompts, RAG results, tool outputs, memory, and conversation history -- is the skill that matters now. Here is what changed and why.
P.10A technical deep dive into DeepSeek V4's Engram conditional memory, Manifold-Constrained Hyper-Connections, and Sparse Attention -- the three innovations enabling million-token context at a fraction of the cost. Benchmarks, architecture diagrams, and what it means for your stack.
P.11EditorPickFebruary 2026 saw an unprecedented wave of AI model releases from OpenAI, Anthropic, Google, and others. We break down GPT-5.3 Codex, Claude Opus and Sonnet 4.6, Gemini 3.1 Pro, DeepSeek V4, and every major launch -- with benchmarks, pricing, and practical guidance.
P.12A systematic comparison of modern RAG approaches in 2026: ColBERT, SPLADE, hybrid search, contextual retrieval, and late interaction models. Benchmarks, architecture tradeoffs, and when RAG beats fine-tuning.
P.13A hands-on guide to running Llama 4, Qwen3, Phi-4, and Mistral on consumer GPUs like the RTX 4090 and 5090. Covers quantization formats, inference engines, VRAM needs, and when local beats API calls.
P.14EditorPickClaude Sonnet 4.6 matches Opus performance at Sonnet pricing. Full breakdown of benchmarks, features, adaptive thinking, and what it means for developers.
P.15Stop guessing which AI approach to use. This decision framework with real cost, latency, and accuracy comparisons helps you pick the right one every time.
P.16EditorPickNaive RAG is broken. Here is how contextual retrieval, hybrid search, and intelligent chunking are reshaping how we build AI applications in 2026.
P.17DeepSeek's V4 model brings 1 trillion parameters, Engram conditional memory, and open-source weights under Apache 2.0. We break down the architecture, coding benchmarks, geopolitical implications, and what it means for developers.
P.18EditorPickThe line between web development and AI development has dissolved. The best agencies now ship web apps with built-in intelligence — chatbots, predictive features, automated workflows. Here's what this shift means.
P.19DeepSeek and Alibaba's Qwen surged from 1% to 15% global AI market share in a single year. With 700M+ Hugging Face downloads, open-source AI from China is reshaping enterprise choices, developer workflows, and the competitive landscape.
P.20EditorPickThree approaches to customizing AI for your use case, with cost comparisons, performance benchmarks, implementation timelines, and a decision framework. The guide we wish existed when we started.
P.21Traditional test suites break when outputs are non-deterministic. Here's how we test AI-powered features — from LLM output validation to regression testing for prompt changes, with real frameworks and examples.
P.22EditorPickMulti-agent systems sound great in demos but break in production. Here's how to architect, orchestrate, and monitor AI agent teams that reliably handle complex workflows — patterns from real deployments.
P.23The AI industry is pivoting from massive models to efficient SLMs offering 10-30x reductions in latency and cost. Learn why smaller is better and how to leverage SLMs in your applications.