AI / ML — Page 8 of 9
Browsing page 8 of 9 — 99 articles on ai / ml.
AI Guardrails in Production: Content Filtering, Output Validation, and Safety Layers for LLM Applications
Most teams ship LLM features with a system prompt and hope for the best. This article covers the full guardrails stack for production: input validation, prompt injection detection, output filtering, structured output validation with Zod, PII detection, topic boundaries, and cost guardrails, layered without destroying latency.
Embedding Models Compared: OpenAI, Cohere, and Open-Source Models for Production Search
Choosing an embedding model affects retrieval quality, cost, latency, and operational risk in ways most teams underestimate. This guide compares OpenAI text-embedding-3, Cohere embed-v3, and open-source options including sentence-transformers, nomic-embed, and BGE, with TypeScript examples and a concrete decision framework.
Prompt Engineering for Production: Versioning, Testing, and Deployment Pipelines
Prompt engineering in production is a software discipline, not a creative exercise. This guide covers versioning strategies, testing pipelines, A/B testing, deployment patterns, and observability for teams running prompts at scale.
Building AI Agents That Actually Work: Orchestration Patterns for Production
Most AI agent demos use a simple loop that collapses under real-world constraints. This article covers the orchestration patterns that make agents reliable in production: topology design, tool-use, memory management, error recovery, observability, and cost guardrails.
Building Hybrid Search: Combining Vector Embeddings and BM25 for Production Retrieval
Most teams pick either keyword search or vector search and miss the sweet spot. This article covers how BM25 and vector similarity complement each other, reciprocal rank fusion scoring, TypeScript implementation with Postgres pgvector and full-text search, embedding model selection tradeoffs, reranking with cross-encoders, and production tuning for relevance.
Fine-Tuning vs RAG: How to Choose the Right LLM Customization Strategy
Most teams default to RAG because it is simpler to set up, but fine-tuning solves fundamentally different problems. This article breaks down the architecture of each approach, compares cost, latency, and accuracy tradeoffs with real numbers, and gives a concrete decision framework based on whether the knowledge you need to add is behavioral or factual.
LLM Cost Optimization in Production: Token Budgets, Semantic Caching, and Model Routing
Most teams overspend on LLM inference by 3-10x because they treat every request identically. This guide covers the full cost reduction stack: token budget enforcement, semantic caching with embeddings, intelligent model routing, prompt compression, and batch vs real-time tradeoffs. TypeScript throughout.
LLM Gateway Architecture: Rate Limiting, Caching, and Routing Across Multiple Providers
Production AI systems need more than a direct API call to a single LLM. This guide covers the full LLM gateway layer: semantic caching, provider routing with fallback chains, per-model rate limiting, request normalization, and token-level observability. TypeScript throughout.
Streaming LLM Responses in Production: Server-Sent Events, Backpressure, and Edge Delivery
How to stream LLM output to users in real time: SSE vs WebSocket tradeoffs, backpressure when the model generates faster than the client consumes, token buffering strategies, edge delivery via Cloudflare Workers, error handling mid-stream, and cost implications.
Structured LLM Output in Production: JSON Mode, Function Calling, and Constrained Decoding
Most teams start with parsing JSON from raw LLM responses and hit malformed output at scale. This article covers the three main approaches to reliable structured output, their production failure modes, validation strategies, fallback chains, and how to choose based on your latency and reliability requirements.
Multi-Agent Orchestration: Patterns for Coordinating LLM Agents in Production
Single agents hit real limits at scale. This guide covers the four core multi-agent patterns (supervisor, pipeline, debate/consensus, map-reduce) with TypeScript implementations, state management strategies, error handling, and the failure modes that emerge when agents interact.
How to Evaluate LLM Output Quality in Production
A practical framework for evaluating LLM output quality in production, including offline test sets, online metrics, LLM-as-judge caveats, human review loops, guardrails, and release gates that prevent silent regressions.