AI.info

Implementation Guides

Browse Implementation Guides on AI.info.

• Deploying LLMs in Production: A Complete Guide

  Everything you need to know about serving large language models at scale. Covers model serving frameworks, quantization, batching, scaling, and cost optimization.

• Data Quality Framework for AI Projects

  A practical guide to establishing data quality standards for AI and ML projects. Covers data profiling, validation rules, monitoring, and governance best practices.

• Implementing Real-Time ML Inference

  How to build low-latency ML inference systems that serve predictions in milliseconds. Covers model optimization, serving architectures, caching, and performance tuning.

• Building AI-Powered Recommendation Systems

  A practical guide to building recommendation engines using collaborative filtering, content-based methods, and modern deep learning approaches. From design to deployment.

• GPU Infrastructure Management for ML Teams

  How to plan, provision and manage GPU compute for machine learning: workload tiering across Hopper, Blackwell and Rubin, Slurm versus Kubernetes, MIG, spot economics and DCGM monitoring.

• CI/CD for Machine Learning Projects

  How to implement continuous integration and deployment for ML systems. Covers testing strategies, model validation gates, automated retraining, and deployment pipelines.

• Inference Economics: Quantization, Speculative Decoding, and the Art of Serving LLMs Cheaply

  PagedAttention, continuous batching, KV-cache pricing, quantization, speculative decoding, MoE routing and batch tiers — and why the per-token floor keeps falling while a $10/$50 premium tier reopened above it.

• Building a RAG System from Scratch

  A complete step-by-step guide to building a production-ready Retrieval-Augmented Generation system. Covers document processing, chunking, embeddings, vector stores, hybrid retrieval with Cohere Rerank 4, cited generation, and evaluation.

• Securing LLM Applications in Production: Prompt Injection, Jailbreaks, and the New Attack Surface

  A defensive guide to the OWASP LLM Top 10: real incidents from the Bing Sydney leak to EchoLeak and the 2026 Word injection worm, why prompt injection resists patching, and the guardrail, red-teaming and compliance controls that hold up now the EU AI Act is in force.

• Vector Search Implementation Guide

  A hands-on guide to shipping vector similarity search: choosing an embedding model, HNSW vs IVF vs PQ, pgvector 0.8.6 iterative scans, hybrid BM25 fusion with RRF, Cohere Rerank 4, and recall tuning.