How to run HyDE, Multi-Query Retrieval, and Step-Back Prompting in a production RAG pipeline. Working Python code, latency and cost benchmarks, an adaptive routing classifier, and a RAGAS evaluation loop for picking the right rewriter per query.
Semantic routing sends each LLM query to the right handler via embedding similarity, not a router prompt. Cut routing latency 20x with Aurelio semantic-router, thresholds, and a working Python example.
A working comparison of RAGAS 0.2, TruLens 1.x, and DeepEval 2.x for evaluating RAG pipelines: metrics, judge models, CI wiring, and where each one actually fits.
Benchmarks and drop-in code for the four rerankers that actually matter in 2026: Cohere Rerank 3.5, Voyage Rerank-2, Jina Reranker v2, and BGE v2-M3. Pick the right one for your RAG pipeline.
Compare the four leading LLM observability platforms (Langfuse, LangSmith, Helicone, and Arize Phoenix) with real production patterns for tracing, prompts, and evals.
Compare Claude Extended Thinking, OpenAI o3, and Gemini 2.5 Thinking in real production workloads. Pricing per solved problem, tool-use behavior, latency, and the eval loop I use before shipping any reasoning model to real users.
Compare the four leading AI browser agents in 2026: Browser Use, Stagehand, Skyvern, and Anthropic Computer Use. WebVoyager benchmarks, real cost figures, production code, and a five-question framework to pick the right stack for your workflow.
Build a deep research agent in Python with LangGraph. Get planning, parallel search sub-agents, a reflection loop, and cited synthesis with runnable code and real cost figures.
A production comparison of Pinecone, Qdrant, Weaviate, and Milvus in 2026: benchmarks, pricing, hybrid search, filter models, and code samples. Includes a decision matrix and a pgvector fallback for teams under 10M vectors.
Circuit breakers, fallback chains, hedging, and bulkheads for LLM apps: patterns that keep OpenAI, Anthropic, and Gemini outages from taking you down.
Design JSON Schemas for LLM function calling that hold up under real production traffic. Strict mode, enums, nesting depth, Pydantic-first, and the failure modes I measure across OpenAI, Anthropic, and Gemini.
A field guide to picking between Zapier, Make, n8n, and Pipedream in 2026: how each platform charges, where each one wins, and the workflow shapes that decide the choice faster than any feature list.
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