AI / ML — Page 4 of 9
Browsing page 4 of 9 — 99 articles on ai / ml.
Building a RAG Pipeline for Code Search: Repository Indexing, Semantic Retrieval, and Context Assembly for AI Development Tools
Code search is not document search. This guide covers the full pipeline for RAG over codebases: AST-aware chunking, code embedding model selection, vector store indexing with metadata filters, re-ranking, context window assembly, and incremental indexing on git push.
Building a Semantic Search Engine: Embedding Pipelines, Approximate Nearest Neighbors, and Relevance Tuning in Production
A practical engineering guide to building a production semantic search system, covering embedding model selection, batched inference pipelines, ANN index types, hybrid BM25 scoring, re-ranking, and latency budgets.
Building LLM-Powered Code Review Pipelines: Static Analysis, AI Review, and Developer Workflow Integration
How to build a production LLM code review pipeline covering git hook integration, CI orchestration, diff chunking for large PRs, confidence scoring with auto-approve logic, GitHub and GitLab API integration, cost management, and measuring review quality over time.
Building a Real-Time Speech Analytics Pipeline: Transcription, Sentiment Detection, and Conversation Intelligence in Production
How to architect a production speech analytics system that processes audio streams in real time, covering STT provider selection, speaker diarization, sentiment detection, conversation intelligence features, and cost modeling.
Building an AI-Powered Compliance Engine: Automated Policy Checks, Document Verification, and Audit Trail Generation for Regulated Startups
A production guide to building an AI compliance engine that automates policy checks, evidence collection, document verification, and immutable audit trail generation for SOC 2, HIPAA, and PCI DSS.
Building an LLM-Powered Data Extraction Pipeline: Schema Mapping, Validation Chains, and Handling Messy Real-World Documents
Most LLM extraction pipelines work fine on clean test PDFs and fail on the actual invoices, contracts, and medical records that show up in production. This article covers document preprocessing, schema-driven extraction with function calling, multi-layer validation, failure handling, and cost management for production extraction systems.
Building a Conversational Analytics System: Natural Language Queries, Dashboard Generation, and Data Storytelling with LLMs
How to build a system that lets users ask questions about their data in plain English and get charts, tables, and narrative answers back. Covers intent classification, text-to-SQL with schema context, visualization selection, narrative summarization, and caching with guardrails for safe SQL execution.
Building an Agentic RAG System: Query Planning, Multi-Source Retrieval, and Self-Correcting Answer Pipelines
Naive RAG fails on complex queries. This guide covers the full architecture of agentic RAG: query decomposition with LLMs, multi-source retrieval orchestration across vector DBs, SQL, and APIs, answer synthesis with source attribution, and self-correction loops with TypeScript code throughout.
Building an AI-Powered Pricing Engine: Demand Forecasting, Dynamic Pricing, and Revenue Optimization for SaaS Products
A practical guide to building a production pricing engine for SaaS: feature engineering for pricing signals, time-series demand forecasting, price elasticity modeling, safe A/B testing, willingness-to-pay estimation, and the guardrails that prevent pricing disasters.
From AI Prototype to Production: The Engineering Playbook for Scaling LLM Applications
Most LLM prototypes die in staging. This guide covers the failure modes that only appear at scale, the production readiness checklist engineers skip, and the infrastructure decisions that determine whether your AI feature ships or stalls.
Securing AI-Generated Code in Production: Static Analysis, Vulnerability Scanning, and Governance Pipelines for Vibe-Coded Applications
40-62% of AI-generated code contains security vulnerabilities. This guide covers building a CI/CD security pipeline with Semgrep, secret detection, dependency scanning, runtime monitoring, and a practical governance framework for teams using Copilot, Cursor, or Claude Code.
AI Red Teaming in Production: Adversarial Testing, Prompt Injection Defense, and Safety Evaluation for LLM Applications
A practical guide to adversarial testing for production LLM systems. Covers the threat model, TypeScript red team evaluation frameworks, automated attack suites, layered defenses, quantitative safety metrics, and CI/CD integration.