Guides
147 items
- GuideAgentic AI Frameworks
Managing Agent State Across Sessions: Databases, Checkpoints, and Resumption
This guide explores strategies for managing agent state in long-running AI workflows. It compares storage options like databases and checkpointing techniques, evaluates resumption methods, and offers best practices for engineering resilient agentic systems.
- GuideRAG Pipelines & Patterns
Managing Latency in Agentic RAG Systems
This guide analyzes latency factors in agentic Retrieval-Augmented Generation systems, providing enterprise AI teams with concrete approaches to optimize response times in performance-sensitive environments. It covers architectural considerations, caching, query optimization, and agent orchestration.
- GuideAI Cost, FinOps & TCO
Measuring Pilot ROI: Success Metrics for 90-Day Trials
This guide provides program managers with a structured approach to measuring the return on investment (ROI) for 90-day AI and technology pilot programs. It outlines key success metrics, data collection methods, and evaluation frameworks necessary to assess pilot outcomes in enterprise settings.
- GuideModel Evaluation & Benchmarking
Metrics That Matter for LLMs: Latency, Tokens, Hallucination, Drift
This guide details four critical metrics for managing large language models (LLMs) — latency, token usage, hallucination, and model drift — with a focus on their operational impact and measurement methods for MLOps engineers.
- GuideRAG Pipelines & Patterns
Migrating Between Vector Databases: Export, Import, and Zero-Downtime
This guide details a step-by-step approach to migrating production vector databases with minimal disruption. It covers data export and import strategies, schema compatibility considerations, and approaches to achieving zero-downtime migration for retrieval-augmented generation (RAG) and knowledge systems.
- GuideAI Governance & Compliance
Model Documentation for Compliance: Model Cards and FactSheets
This guide provides governance teams with a structured approach to using model cards and FactSheets for AI model documentation to meet compliance requirements. It details the key components, recommended practices, and implementation considerations for effective model risk management.
- GuideMLOps & Model Deployment
Model Monitoring Alert Tuning: Reducing Noise
This guide offers actionable strategies for tuning model monitoring alerts to minimize noise and maintain signal relevance. It targets MLOps professionals responsible for model reliability, providing techniques drawn from industry benchmarks and platform features.
- GuideMLOps & Model Deployment
Model Monitoring in Production: Drift, Performance, and Anomaly Detection
This guide explores key components of model monitoring in production environments, focusing on data drift, performance degradation, and anomaly detection. It provides practical approaches and tools tailored for MLOps teams tasked with sustaining model quality and managing risk.
- GuideFoundation Models
Model Pruning for Production: Removing Unused Weights
A step-by-step guide for ML engineers on model pruning techniques to reduce model size and inference costs by removing unused weights without compromising accuracy.
- GuideAI Risk Management
Model Remediation Playbook: When Models Fail Compliance
This guide outlines a systematic approach for enterprise teams to address AI model compliance failures, covering initial detection, impact assessment, remediation strategies, and prevention measures. It is designed for AI risk officers, platform engineering leads, and compliance managers managing regulated AI deployments.
- GuideAI Security
Model Theft Prevention: Watermarking, Obfuscation, and API Rate Limiting
This guide provides enterprise AI buyers and platform teams with tactical methods to protect proprietary machine learning models. It covers three key strategies: digital watermarking to embed ownership signals, model obfuscation to complicate extraction, and API rate limiting to reduce abuse risk.
- GuideModel Evaluation & Benchmarking
Model Validation for AI: Beyond Accuracy to Robustness and Fairness
This guide outlines critical dimensions of AI model validation extending beyond traditional accuracy metrics. It focuses on robustness, fairness, and compliance considerations essential for effective model risk management in enterprise environments.
- GuideMLOps & Model Deployment
Model Version Control and Rollback for Compliance
This guide covers best practices and architectural considerations for implementing model version control and rollback in ML platforms to meet regulatory and internal compliance requirements. It discusses tooling options, auditability, and risk mitigation strategies essential for enterprise ML governance.
- GuideRAG Pipelines & Patterns
Multi-Lingual Embeddings for Global Enterprises
This guide examines multi-lingual embeddings tailored to enterprises managing non-English document collections. It covers key model architectures, vendor offerings, cost considerations, and implementation challenges for retrieval-augmented generation (RAG) and knowledge applications.
- GuideRAG Pipelines & Patterns
Multi-Modal RAG: Retrieving Images, Tables, and Text Together
This guide explores how multi-modal retrieval-augmented generation (RAG) architectures integrate images, tables, and text to enhance document AI capabilities. It outlines core components, challenges, and emerging vendor solutions supporting enterprise-scale deployments.
- GuideRAG Pipelines & Patterns
Multi-Tenant RAG for B2B SaaS: Isolating Customer Knowledge
This guide explains how product teams can implement Retrieval-Augmented Generation (RAG) in multi-tenant B2B SaaS environments to securely isolate customer knowledge bases. It covers architecture patterns, data segmentation strategies, and operational considerations for enterprise-grade knowledge management.
- GuideRAG Pipelines & Patterns
Multimodal RAG: Retrieving Images, Charts, and Tables
This guide explains how to implement and optimize multimodal Retrieval-Augmented Generation (RAG) workflows that retrieve not only text but also images, charts, and tables from documents. It covers architecture choices, indexing techniques, model integration, and operational considerations specific to enterprise AI use cases.
- GuideAI Vendor Selection
Negotiating LLM API Contracts: Volume Discounts, SLAs, and Data Terms
This guide outlines key negotiation points for enterprise procurement teams engaging with large language model (LLM) API providers. It focuses on structuring volume discounts, securing service level agreements (SLAs), and clarifying data usage and privacy terms to align cloud and AI governance requirements.
- GuideAI Governance & Compliance
NYDFS Part 500: AI Governance in Financial Services
This guide outlines how financial institutions subject to the New York Department of Financial Services (NYDFS) Part 500 cybersecurity regulation can approach governance of artificial intelligence deployments. It highlights key compliance requirements, governance practices, and enforcement expectations relevant to banks and insurers.
- GuideAI Security
PII Detection and Redaction for LLM Inputs and Outputs
This guide provides a methodical approach for privacy teams on detecting and redacting Personally Identifiable Information (PII) in inputs and outputs of Large Language Models (LLMs). It reviews technical strategies, toolsets, and compliance considerations to mitigate data leakage risks in AI deployments.
- GuideEnterprise AI Readiness & Adoption
Presenting AI to the Board: Slides, Data, and Talking Points
This guide provides AI leaders with a detailed framework for preparing and delivering board presentations on AI initiatives, covering slide structure, critical data points, and effective talking points. It aims to improve decision-making by aligning AI proposals with business objectives and financial metrics.
- GuideAI Governance & Compliance
Privacy-Preserving AI for GDPR and HIPAA Compliance
This guide explores methods and architectures for deploying AI systems that meet the data minimization requirements under GDPR and HIPAA. It covers key compliance considerations, technical approaches like federated learning and differential privacy, and vendor tools that support privacy-preserving AI.
- GuideEnterprise AI Readiness & Adoption
Prompt Engineering for Business Users: A Non-Technical Guide
This guide offers business users a step-by-step approach to prompt engineering, enabling effective interactions with AI tools without requiring technical expertise. It includes actionable templates to improve prompt design and maximize AI output quality.
- GuideAI Security
Prompt Injection: The OWASP Top 10 for LLMs and How to Mitigate
An enterprise-focused guide that catalogs the top 10 prompt injection risks identified by OWASP for large language models (LLMs), paired with concrete mitigation strategies. Includes example attack patterns, validation regex snippets, and code-level controls applicable to real-world AI deployments.