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33 articles tagged #LLM.
P.01Clients ask agencies to 'do something with these PDFs' more often than you'd think. Here's how to actually build document extraction pipelines that work in production: OCR, vision models, and structured output.
P.02Both approaches customize LLM behavior for your use case, but they solve different problems. Here is how to decide which one you need, how to know when to use both, and what teams consistently get wrong.
P.03Three leading agent orchestration frameworks, three different mental models. Here's when each one earns its place, what each costs you in complexity, and what the choice looks like when you're debugging at 2am.
P.04An agent that forgets everything when the session ends is a limited tool. Here are the practical patterns for building different kinds of memory into your agents.
P.05When your AI agent needs to run the code it writes, you can't let it touch your production servers. Here's how the main isolation options work and when to use each.
P.06The Vercel AI SDK has become the default for building AI features in JavaScript apps. Here is what it actually does, how its core primitives work, and where the sharp edges still live.
P.07Hallucination is not a bug that gets patched in the next model release. It is a property of how language models work. Here are the patterns that actually reduce it in production systems, and what they cost.
P.08Single-provider AI dependencies are a reliability risk. Routing layers like LiteLLM and OpenRouter let you fall back across providers, cap costs, and try smaller models first. Here is the architecture and when it actually matters.
P.09Add similarity search to your existing Postgres database using pgvector. Real setup, indexing strategies, and when you actually need a dedicated vector database.
P.10Building an AI feature is only half the work. Once it's in production, you need to know when it's drifting, what it's costing, and where it's failing. Here's how to instrument LLM applications properly.
P.11Unit 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.12LLM 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.13Rolling 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.14AI 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.15Getting 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.16Every 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.17Prompt 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.18Explore ZeroDayBench—A new benchmark testing the efficacy of leading LLM agents in discovering and patching unseen security vulnerabilities.
P.19EditorPickPrompt 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.20A 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.21EditorPickFebruary 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.22A 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.23A 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.24EditorPickClaude Sonnet 4.6 matches Opus performance at Sonnet pricing. Full breakdown of benchmarks, features, adaptive thinking, and what it means for developers.
P.25Stop 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.26EditorPickNaive RAG is broken. Here is how contextual retrieval, hybrid search, and intelligent chunking are reshaping how we build AI applications in 2026.
P.27DeepSeek'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.28EditorPickThe 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.29DeepSeek 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.30EditorPickThree 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.31Traditional 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.32EditorPickMulti-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.33The 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.