22 articles
How production LLM systems are actually built: RAG and retrieval, structured outputs, tool calling, evaluation, observability, cost, and the failure modes vendor blogs skip.
First-party Search Console data on an automated agent that generated 225 permutations of a single query over three weeks, produced 3,572 impressions at average position 8.2, never clicked once, and stopped dead on a single day. How to recognise it, why it wrecks your reporting, and what it says about agents on the open web.
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The decision teams reach after their automation platform stops scaling. Where Zapier, Make, n8n and Pipedream genuinely end, the four failure modes that appear before the ceiling does, and how to split a stack so the boring 80 percent stays no-code and the load-bearing 20 percent gets built.
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A decision framework for picking an embedding model in 2026: API vs self-hosted, when dimensions matter, multilingual and multimodal, and how to benchmark on your own data in an afternoon.
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BGE-M3 against OpenAI text-embedding-3: multilingual quality, the hybrid dense-sparse-multivector trick BGE-M3 has and OpenAI does not, hosting cost, and when self-hosting actually pays off.
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Voyage AI embeddings against OpenAI text-embedding-3 for production RAG: retrieval quality on technical and domain corpora, the domain-specific model lineup, reranking, and total cost.
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Cohere Embed v4 against OpenAI text-embedding-3-large: multimodal and PDF embedding, Matryoshka dimensions, VPC deployment, multilingual coverage, and which one fits an enterprise stack.
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What a reranker does, why cross-encoders beat embeddings at final ranking, how much quality it actually adds, what it costs in latency, and how to add one to an existing RAG pipeline.
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Step-by-step RAG architecture tutorial with TypeScript code, retrieval evaluation, and the five failure modes I hit shipping production RAG.
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Real human-in-the-loop AI patterns from production: approval queues, confidence-based routing, post-hoc audit, and active learning. With code and case studies.
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A senior engineer's take on Pinecone vs Qdrant vs Weaviate vs pgvector. Benchmarks, total cost, and the crossover point where self-hosting wins.
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What OpenAI's API actually costs at scale. GPT-5, GPT-5-mini, caching, Batch API, and the cost-control patterns that cut my client bills 60%.
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When to use RAG, when to fine-tune, when to do both. Real cost data, accuracy numbers, and a decision tree for picking the right approach.
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Agentic RAG turns retrieval into a reasoning loop. Here is how to design, evaluate, and ship one without melting your latency budget or your token bill.
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Production guide to OpenAI structured outputs, verified August 2026 against the Responses API: strict mode, schema design, refusals, streaming, and how OpenAI, Anthropic and Gemini compare.
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Defense-in-depth strategies for prompt injection. Channel separation, output filtering, tool scoping, and the OWASP LLM Top 10 in plain English.
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Comparing the LLM eval frameworks engineers actually run in CI. Covers metrics, latency, cost, and which one to pick for RAG, agents, or chat.
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Side-by-side review of the top LLM observability platforms. Cost, integration time, framework support, and which to pick for agents vs RAG vs chat.
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How to design, name, scope, and document tools for reliable LLM tool calling. Parallel calls, error handling, prompt-injection-safe tool design.
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Workflows are predictable and cheap. Agents are flexible and expensive. Here is how to tell which one your problem actually needs, with examples.
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Compares the leading embedding models for RAG. MTEB scores, context length, multilingual support, and price per million tokens.
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The agent patterns that actually ship: reflection, planner-executor, ReAct, multi-agent handoff, and when to use a workflow instead.
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The architecture decisions every AI SaaS converges on: multi-tenant data, per-user limits, model routing, eval pipelines, and BYO-key support.
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