Guides
166 items
- GuideAI Cost, FinOps & TCO
Forecasting AI Spend: Capacity Planning for Growing Usage
This guide helps finance and engineering teams forecast AI expenditures by aligning capacity planning with growing AI usage. It covers key metrics, cost drivers, and practical frameworks to manage and optimize AI spend.
- GuideAI Vendor Selection
How to Read Gartner and Forrester AI Reports
This guide explains how enterprise procurement and strategy teams can interpret Gartner and Forrester AI reports. It covers report types, evaluation criteria, common frameworks, and practical tips to extract actionable intelligence for vendor selection and AI adoption strategies.
- GuideEnterprise AI Readiness & Adoption
How to Select Your First AI Pilot Project
This guide provides a structured approach to selecting an initial AI pilot project, balancing practical business impact with technical feasibility. It includes a prioritization matrix to help enterprise teams make informed decisions aligned with strategic objectives.
- GuideFoundation Models
Human Review for Hallucination-Prone Outputs: Workflow Design
This guide outlines best practices for integrating human review into workflows targeting hallucination-prone outputs in large language models (LLMs). It covers identification strategies, review triggers, reviewer expertise requirements, and audit mechanisms critical for enterprise contexts where accuracy is non-negotiable.
- GuideMLOps & Model Deployment
Implementing Federated Learning with Flower or NVIDIA FLARE
This guide provides ML engineers with a detailed, step-by-step approach to implementing federated learning using Flower and NVIDIA FLARE. It covers architecture overview, setup requirements, installation, workflow orchestration, and evaluation for privacy-preserving AI deployments.
- GuideAI Governance & Compliance
Managing Multiple Regulatory Regimes: EU AI Act + HIPAA + GDPR
Enterprises operating globally face overlapping regulatory requirements from the EU AI Act, HIPAA, and GDPR. This guide outlines practical steps for harmonizing compliance efforts across these regimes, focusing on AI governance, data protection, and cross-jurisdictional operational challenges.
- GuideEnterprise AI Readiness & Adoption
Measuring AI Adoption: Login Data, Feature Usage, and NPS
This guide explains how to leverage login data, feature usage analytics, and net promoter score (NPS) to measure AI adoption effectively. It offers practical insights for product managers and operations leads to track engagement and satisfaction within enterprise AI deployments.
- GuideAI Cost, FinOps & TCO
Measuring AI Productivity Gains: Time Saved vs. Output Increased
This guide examines methodologies for measuring AI productivity gains through metrics focusing on time saved and output increase. It provides best practices for baselining and comparing AI interventions, helping enterprise teams develop reliable ROI frameworks.
- GuideRAG Pipelines & Patterns
Migrating from Pinecone to Open Source: A Step-by-Step Guide
This guide walks enterprise teams through the technical steps required to migrate from Pinecone, a managed vector database service, to an open source alternative. It focuses on aspects essential for cost reduction, including data export, environment setup, indexing, and validation.
- GuideMLOps & Model Deployment
Multi-Region Deployment for Low-Latency Global AI
This guide outlines key architectural considerations and trade-offs for deploying AI models across multiple cloud regions to reduce latency for global users. It covers infrastructure requirements, consistency models, data synchronization, and cost implications.
- GuideRAG Pipelines & Patterns
Multi-Step Retrieval Patterns: Iterative Refinement and Self-Query
This technical guide explores multi-step retrieval patterns focusing on iterative refinement and self-query strategies. It targets enterprise AI builders looking to enhance retrieval-augmented generation (RAG) architectures with agentic approaches for improved contextual accuracy and reasoning.
- GuideAI Vendor Selection
Negotiating AI vendor contracts: SLAs, indemnification, and data rights
This guide provides procurement and legal teams with a detailed framework for negotiating AI vendor contracts. Focus areas include service-level agreements (SLAs), indemnification clauses, and data rights to mitigate risks and control costs.
- GuideAI Governance & Compliance
NIST AI Risk Management Framework: Adoption Guide
This guide provides a detailed, step-by-step approach for enterprises adopting the NIST AI Risk Management Framework (RMF), focusing on practical application across governance, process integration, and technology controls to meet regulatory compliance and security standards.
- GuideAI Cost, FinOps & TCO
Optimizing Prompts for Fewer Tokens (Without Losing Quality)
This guide provides a detailed, step-by-step approach to reducing token count in AI prompts while maintaining output quality. It includes practical examples to illustrate techniques suitable for enterprise AI implementations aiming to control costs and improve inference speed.
- GuideRAG Pipelines & Patterns
Query Rewriting and Expansion for Enterprise Search
This guide provides a systematic approach to applying query rewriting and expansion techniques in enterprise search environments. It covers key methods, implementation considerations, and practical tips for improving search accuracy and user satisfaction.
- GuideFoundation Models
Speculative Decoding for Faster and Cheaper Inference
This guide explains speculative decoding, a technique that accelerates large language model inference while reducing computational cost. It covers the method's architecture, implementation considerations, and trade-offs for enterprise AI engineers seeking cost-effective model serving.
- GuideEnterprise AI Readiness & Adoption
Stakeholder Mapping for AI Initiatives: Who Needs to Approve What
This guide outlines how program managers can effectively identify, categorize, and manage stakeholders in AI projects, clarifying approval responsibilities to accelerate enterprise decision-making. It covers key stakeholder roles, common approval bottlenecks, and best practices for structured engagement.
- GuideData Engineering for AI
Synthetic Data Generation for Privacy-Preserving AI
This guide covers the use of synthetic data generation techniques, specifically large language models (LLMs) and generative adversarial networks (GANs), for creating privacy-preserving test data. It details methods, challenges, and considerations relevant to enterprise AI buyers and platform leads.
- GuideAgentic AI Frameworks
Tool Calling Deep Dive: Function Definitions, Schema Design, and Error Handling
This guide explores best practices for implementing agent tools, focusing on defining functions, designing schemas for tool communication, and managing error handling effectively. It addresses common pitfalls and offers decision-support for platform engineers and developers building AI agent toolchains.
- GuideEnterprise AI Readiness & Adoption
AI CoE Training Programs: Upskilling the Enterprise
This guide outlines effective approaches for AI Center of Excellence (CoE) teams to develop training programs that upskill enterprise staff. It covers alignment with CoE priorities, curriculum design, measurement of program impact, and best practices drawn from industry sources.
- GuideAI Cost, FinOps & TCO
AI Cost Observability: Tagging, Budgets, and Alerts
This guide explains how FinOps teams can implement effective cost observability for AI workloads using tagging strategies, enforce budgets, and configure alerts. It covers best practices for granular AI spend breakdowns and monitoring to control AI project costs.
- GuideAI Security
AI Security Training for Developers: OWASP Top 10 for LLMs
This guide outlines how enterprise AI security training programs can integrate the OWASP Top 10 risks for large language models (LLMs) into developer education. It provides actionable steps for security teams to enhance developer understanding and reduce AI-specific vulnerabilities.
- GuideAI Governance & Compliance
Audit Trails for Agents: Recording Every Decision and Action
This guide outlines best practices for creating comprehensive audit trails in autonomous and semi-autonomous agents, focusing on requirements for compliance and security teams to ensure transparency, accountability, and mitigation of operational risks.
- GuideFoundation Models
Automating document processing with multimodal LLMs
This guide outlines the process of implementing multimodal large language models (LLMs) for automating document processing tasks in enterprise settings. It covers structured and unstructured document types, including invoices, forms, and contracts, highlighting model selection, data preparation, integration strategies, and evaluation metrics.