AI / ML — Page 3 of 9
Browsing page 3 of 9 — 99 articles on ai / ml.
Building an LLM Evaluation Harness: Automated Scoring, Human-in-the-Loop Review, and Regression Testing for Prompt Changes
Most LLM evaluation advice stops at "use LLM-as-judge." This article goes further, covering how to engineer a full evaluation harness in TypeScript with golden datasets, multi-method scoring, human review queues, and CI regression gates that block prompt regressions before they ship.
Multimodal RAG in Production: Retrieving and Reasoning Over Images, Tables, and Code Alongside Text
How to build a production multimodal RAG pipeline that handles images, tables, code, and PDFs in a unified retrieval architecture alongside text, including embedding models, chunking strategies, cross-modal retrieval, and evaluation.
Spec-Driven Development for AI Engineering Teams: Writing Specifications That AI Agents Can Execute
Vague prompts produce vague code. This guide covers how to structure specifications for AI coding agents: acceptance criteria, interface contracts, constraints, test cases, and a complete spec-to-code workflow with real TypeScript examples showing good vs bad specs and their actual output quality.
Building an AI-Powered Workflow Automation Engine: Task Classification, Decision Routing, and Human-in-the-Loop Orchestration
78% of enterprises have agentic AI pilots. 14% run them in production. This guide covers what the gap actually is: task classification with confidence thresholds, rule-based vs ML routing, human escalation patterns, audit trails, and monitoring automation accuracy in real systems.
LLM Observability in Production: Tracing Chains, Measuring Latency, and Debugging Agent Failures
How to instrument LLM-powered applications for production: tracing multi-step chains and agent loops, measuring token usage and latency per step, detecting prompt regressions, structured logging, cost attribution, and debugging agent failure modes.
Time Series Forecasting in Production: Feature Engineering, Model Selection, and Deployment Patterns for Real-Time Predictions
A practical guide to building time series forecasting systems that hold up in production: feature engineering, model selection tradeoffs, walk-forward validation, and serving patterns for real-time predictions.
Building a Multimodal AI Pipeline: Processing Images, Audio, and Text with LLMs in Production
A practical guide to building production multimodal AI systems that process images, audio, and text. Covers input preprocessing, modality routing, model orchestration, fusion strategies, latency management, cost control, and partial-failure handling.
LLM Fine-Tuning Infrastructure: Training Pipelines, Dataset Management, and Deployment Strategies for Custom Models
A practical guide to fine-tuning LLMs in production. Covers when fine-tuning actually beats RAG or prompt engineering, dataset preparation and versioning, LoRA/QLoRA training pipelines, multi-GPU compute provisioning, evaluation frameworks, model registry, A/B deployment, and rollback strategies.
LLM Structured Output in Production: JSON Mode, Function Calling, and Schema-Validated Response Patterns
Unstructured LLM output breaks downstream systems. This guide covers JSON mode across providers, function calling patterns, Zod/TypeBox schema validation, retry strategies for malformed output, streaming structured responses, and the real tradeoffs between constrained generation and post-processing validation.
AIOps in Practice: Using LLMs for Log Analysis, Incident Correlation, and Automated Root Cause Analysis
A practical guide to applying LLMs to operational intelligence. Covers log pattern extraction with embeddings, incident correlation across services, automated root cause analysis pipelines, alert noise reduction, and honest cost/latency tradeoffs.
Building an AI-Powered Code Migration Pipeline: AST Parsing, LLM-Driven Refactoring, and Automated Validation for Framework Upgrades
A practical guide to building a code migration pipeline that combines AST parsing with LLM-driven refactoring for framework upgrades. Covers ts-morph integration, prompt construction from AST context, validation strategies, and how to handle the 20% of cases where LLMs need human oversight.
RAG Pipelines in Production: Chunking Strategies, Retrieval Tuning, and the Failure Modes Nobody Talks About
Beyond the basic RAG tutorial: chunking strategies, embedding model selection, HNSW tuning, retrieval quality measurement, reranking, and the failure modes that only surface at scale.