Industry best practices and patterns
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.
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.
LLM-as-a-Judge in production: cancel positional, verbosity, and self-preference bias, calibrate against Cohen's kappa with 200+ human labels, and wire reliable automated evaluation into your CI pipeline without breaking the budget.
Production LLM streaming over SSE: fix nginx buffering, propagate cancellation to the provider, handle backpressure, and instrument TTFT for OpenAI, Anthropic, and Gemini.
A production playbook for handling HTTP 429 responses from OpenAI, Anthropic, and Gemini in 2026: retry-after, exponential backoff with jitter, token buckets, circuit breakers, and multi-provider fallback.
Parallel tool calls cut agent latency by 40-70%, but the OpenAI, Anthropic, and Gemini APIs behave differently. Runnable Python for each provider, the opt-out flags that matter, and paired-eval methodology for deciding when to serialize.
A practical, evaluation-first guide to shipping text-to-SQL with LLMs in production: schema linking as RAG, semantic layers, three-stage query validation, and defense-in-depth guardrails, with runnable code and 2026 benchmark numbers.
Voyage-3-large tops MTEB v2, Cohere Embed v4 handles 128K multimodal, OpenAI is the best default, and Jina wins multilingual. Full 2026 benchmark with code.
OpenAI Batch API and Anthropic Message Batches both cut LLM token costs by 50% in exchange for a 24-hour SLA. Here is when batch wins, working Python for both, and the production pitfalls nobody mentions.