20 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.
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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