AI / ML Toolkits
RAG pipelines, multi-agent frameworks, prompt engineering packs, MLOps blueprints, and vector database designs. Frequently Asked Questions What RAG pipeline architectures are included in the AI/ML toolkits? The RAG toolkits include hybrid retrieval architectures combining dense embeddings with BM25 sparse retrieval, reranking pipelines using Cohere and cross-encoder models, and citation grounding patterns validated against enterprise document stores with 2+ million documents. Each architecture includes chunking strategies (fixed-size, semantic, and recursive), embedding model selection guides comparing OpenAI, Cohere, and open-source options, and retrieval performance benchmarks with precision/recall metrics at different k values. Which multi-agent AI frameworks are covered in the collection? The collection provides orchestration patterns for Claude, GPT-4, and open-source models using LangChain, LangGraph, and CrewAI. Each framework includes agent memory management with short-term and long-term storage patterns, tool integration blueprints for 15+ common tools (web search, code execution, database queries), error recovery strategies with retry logic and fallback chains, and cost tracking dashboards that break down spend per agent per task. Do the MLOps blueprints support model training on AWS SageMaker and Google Vertex AI? Yes. The MLOps blueprints include end-to-end training pipelines for SageMaker, Vertex AI, and Azure ML with experiment tracking via MLflow. Each pipeline covers data versioning with DVC, automated hyperparameter tuning, model registry management with promotion gates, and automated retraining triggered by data drift detection using Evidently AI. Deployment patterns include A/B serving, shadow deployments, and feature flag-based rollouts. What vector database options are included for building AI search applications? The toolkit covers four vector databases — Pinecone, Weaviate, Qdrant, and pgvector — with detailed indexing strategies, embedding model selection guides, and retrieval performance benchmarks for each. You get schema design patterns for hybrid search (combining vector similarity with metadata filtering), cost comparison calculators, and migration scripts for moving between providers. Visit our Multi-Industry AI collection for sector-specific AI implementations built on these foundations. How many prompt engineering templates are included in the AI/ML collection? The prompt engineering packs include 200+ tested templates covering code generation, document analysis, data extraction, and multi-step reasoning tasks across regulated industries. Each template comes with version-controlled variants, A/B testing results showing performance differences, and evaluation harness configurations using RAGAS and custom metrics. Templates are organized by use case (summarization, classification, generation, extraction) with guidance on when to use few-shot versus chain-of-thought approaches.
